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In Indonesia, there is a rich diversity of cultures, one of which is traditional houses. Traditional houses essentially have the potential to represent the way of life, culture, and local economy. Traditional houses in Indonesia, particularly in the Malaka region, are important cultural symbols that can be regarded as cultural icons in Malaka and Indonesia. They provide a historical perspective, heritage, and reflect the progress of society in a civilization. The Convolutional Neural Network (CNN) method is used in this research. In this study, the CNN algorithm is applied to classify traditional house objects. These traditional house objects are divided into two categories: Kolibein Traditional House and Laleik Traditional House. The objective of this research is to classify traditional houses in Malaka, namely Kolibein Traditional House and Laleik Traditional House, and also to determine the accuracy level of CNN classification results. The previously created model is tested using test data to assess its accuracy. The testing is conducted on 20 data points, with 10 data points in each respective class. The testing results show that the classification of Kolibein and Laleik traditional houses is error- free or very accurate. Based on the model developed for classifying Kolibein and Laleik traditional houses using the Convolutional Neural Network method, it is evident that this method is capable of producing accurate results. The obtained results indicate that the accuracy, based on the classification report using images of Kolibein and Laleik traditional houses, reaches 100%. Therefore, it can be concluded that the constructed CNN model has a high level of accuracy.

Salak is a potential horticultural sector that is a leading commodity in Banjarnegara. Salak fruit varieties have fruit categories that have their advantages. Variants of salak fruit include ivory salak, granulated sugar salak, pondoh salak, and honey salak. Based on data released from the relevant government agencies, further research was carried out related to analyzing and conducting research to predict salak fruit varieties. This variety is suitable for land in every area in Banjarnegara with predictive analysis using the C4.5 algorithm. This method has been widely developed to classify and predict a case with a fairly high degree of accuracy. From this study, researchers hope that it can contribute farmers to determining the type of salak fruit that is most suitable for the land they own so that later the harvest obtained by farmers can be maximized

Menentukan sumber pembelajaran pendukung yang efektif dalam E-Learning merupakan suatu permasalahan yang kompleks, karena keputusan yang diberikan untuk berbagai kriteria yang ditujukan untuk memberi prioritas kepada alternatif yang digunakan. Pendekatan Fuzzy-Analytic Hierarchy Process (Fuzzy AHP) termasuk salah satu prosedur yang bisa menjadi solusi dari permasalahan ini. Metode ini dapat digunakan untuk menentukan keputusan dalam penaksiran kepada kriteria yang sudah ada sebelumnya. Dari perhitungan pengujian yang dilakukan, YouTube mendapat peringkat tertinggi yang lalu dibawahnya diikuti oleh Google, Brainly, dan yang terakhir Slide Player. Dari perhitungan tersebut, dapat disimpulkan bahwa YouTube bisa dijadikan sebagai sumber pembelajaran pendukung dalam sistem pembelajaran E-Learning.

Saat ini seluruh penduduk dunia berjuang melawan wabah covid-19. Cara yang efektif melawan covid adalah melawannya dengan meningkatkan imut tubuh. Salah satu cara meningkatkan imun tubuh adalah menjaga hidup sehat dengan kebiasaan berolahraga. Namun faktanya sebagian besar orang cenderung enggan berolahraga dengan berbagai alasan. Maka penelitian ini menganalisis bagaimana motivasi masyarakat dalam berolahraga selama pandemic Covid-19 dengan analisis core drive Octalysis sehingga dapat digali lebih jauh doronga-dorongan apa saja yang mungkin lemah dan yang kuat sehingga bisa dijadikan referensi bagi pihak-pihak yang berkepentingan untuk menyelenggarakan kegiatan olahraga yang lebih diminati. Hasil analisis menunjukkan bahwa kecenderungan masyarakat berolahraga semasa pandemic Covid-19 adalah termotivasi ingin menjaga kesehatan diri sendiri dan menginspirasi orang lain untuk hidup sehat. Sedangkan suasana kompetisi maupun kerjasama dalam olah raga tidak terlalu dibutuhkan karena masyarakat tidak terlalu termotivasi dari dua hal tersebut.

In today's swiftly changing digital realm, social media has emerged as a pervasive means of communication, yet it has also fostered the rise of cyberbullying, especially among young demographics. This research strives to develop an application that assesses public sentiment on Instagram regarding cyberbullying instances, categorizing sentiments as positive, negative, or neutral. Drawing data from Instagram accounts such as kumparandotcom, merdekadotcom, and okezonedotcom, the approach combines lexicon-based text labeling and sentiment analysis employing Sentistrength. Findings demonstrate the method's effectiveness, achieving accuracy, precision, and recall rates exceeding 85% while offering precise visualization of predictions. This study contributes to combatting cyberbullying, aiming to improve victims' mental well-being by providing clearer insights into social sentiment. The dataset comprises 4500 comments collected through web crawling, categorized into positive (735 entries), negative (2478 entries), and neutral (1288 entries) sentiments. The evaluation highlights the commendable performance of Sentistrength, achieving the highest accuracy at 93.85%.

Indonesia’s natural resources are abundant, including oil and gas. It is one of the countries active in international trade, including exports and imports. Oil and gas exports are a significant source of income for the country, encouraging economic growth. Oil and gas imports are very important to meet domestic energy needs, which continue to increase in demand. Increasing oil and gas imports can increase the trade balance, which can affect the country’s economic stability if the value of imports exceeds the value of exports. Forecasting is a solution to overcome these problems by forecasting the value of oil and gas exports and imports. The gated recurrent unit (GRU) method is used for forecasting in this study because it has a simple computation and fairly high accuracy. The dataset used is monthly time series data from 1993 to 2023 from the website of the Badan Pusat Statistik (BPS). The MAPE results on the GRU model forecast the value of oil and gas exports and imports at 12.19% and 14.30%, respectively. The best average forecasting of export and import values obtained a MAPE of 13.38%.

As a country with abundant natural resources in the form of mineral and non-mineral products, Indonesia is characterized by its ability to fulfill domestic and foreign needs through export activities categorized into two commodities: oil and gas and non-oil and gas. Export activities are an indicator of the country's economic growth that often fluctuates in value, and these conditions are fundamentally caused by a decrease in production quantity and the instability of the global economic climate. The strategy to overcome these problems is to create a forecasting model. This research aims to develop a forecasting model using time series analysis methods, including vector autoregressive (VAR) and long short-term memory (LSTM) methods based on oil and non-oil and gas value parameters. The results of the Granger causality test stated that the values of oil and gas and non-oil and gas affect each other. The VAR model with the optimum lag produced by the Akaike Information Criterion (AIC) test obtained an accuracy value of MAPE oil gas and non-oil and gas of 18.4% and 32.1%, respectively. LSTM generates the best model with a MAPE value of 6,23% for oil gas and 8,18% for non-oil and gas.

Giving appreciation to outstanding students can motivate students to compete with each other in learning. MA Tanwirul Qulub Tanggungan often experiences difficulties in determining outstanding students due to There is no application that can assist school management in identifying outstanding students, the implementation is considered less than optimal. besides that, the determination of outstanding students is still based on report cards that are only ranked, and there are no criteria that refer to the K-13 curriculum. The purpose of this research is to offer a solution to create a recommendation system for selecting outstanding students using the parameters of midterm exams, final exams, assignments, attendance, attitude, extracurricular activities, organizations, and award certificates using decision support system techniques. Extracurricular grades are taken from Scouting activities only because students are generally required to participate in them. Naïve Bayes and Simple Additive Weighting methods are used in this research, where the Naïve Bayes method classifies the categories of outstanding students and not, while the SAW method is used for ranking. The contribution of this research has the potential to increase school efficiency in student assessment and support efforts to improve the quality of education by rewarding students appropriately. The validation test results of Naïve Bayes and SAW techniques get an accuracy value of 100%, which shows that the SAW method can produce the best alternative recommendations

Background: Choosing a banner supplier is a significant challenge for digital printing companies due to the various advantages offered by each supplier, often leading to selections based on subjective aspects such as price and quality. Objective: This research aims to develop a system that determines the best banner supplier to minimize production inefficiencies and maximize profits by comparing two calculation methods, Profile Matching and TOPSIS. Methods: A quantitative study was conducted using transaction data from the last six months. The parameter criteria used in this system include price, quality, delivery, availability, and payment terms. The study compares the effectiveness of Profile Matching and TOPSIS methods in identifying the best supplier. Results: The study results show that the TOPSIS method is superior, yielding 100% accuracy, 84% recall, and a 92% F1-score, outperforming the Profile Matching method. This demonstrates that the correct method and algorithm effectively provide the best alternative recommendations. Conclusion: The results indicate that using the TOPSIS method leads to more accurate and objective decisions based on predetermined criteria. The findings suggest that further research should focus on refining these methods to enhance decision-making in supplier selection.

Prediksi penjualan menjadi elemen penting dalam perencanaan perusahaan karena akan menentukan rencana anggaran penjualan, yang mempengaruhi banyak faktor dalam perusahaan. Produsen penjualan sering kali mengalami kesulitan untuk memprediksi kisaran jumlah produk yang terjual pada periode yang akan datang. Permasalahan tersebut mengakibatkan ketidakefektifan dalam pengelolaan stok dan jadwal produksi, sehingga stok produk digudang menumpuk yang mengakibatkan beberapa produk mengalami kerusakan karena disimpan terlalu lama. Tujuan dari penelitian ini adalah mengimplementasikan metode Simple Linear Regression untuk memprediksi penjualan produk agar dapat mengetahui tentang kisaran jumlah penjualan produk di periode yang akan datang, sehingga dapat menyesuaikan penyedian stok yang lebih efektif berdasarkan prediksi yang diperoleh. Metode Simple Linear Regression digunakan untuk mengevaluasi dan memahami arah serta kekuatan hubungan antara dua variabel, yaitu variabel independen (X) dan variabel dependen (Y). Parameter yang digunakan adalah periode bulan dan jumlah penjualan. Periode bulan yang merujuk pada rentang waktu, bagaimana perubahan waktu dapat memengaruhi jumlah penjualan produk sedangkan jumlah penjualan merupakan output yang menjadi fokus utama, mengetahui sejauh mana variabilitas dalam periode bulan dapat dijelaskan oleh variasi dalam jumlah penjualan produk. Dataset yang digunakan untuk uji tingkat kesalahan terhadap hasil prediksi menggunakan data penjualan mulai periode Februari 2021 sampai dengan September 2023. Hasil uji menyatakan nilai rata-rata dari selisih absolut antara nilai prediksi atau MAD sebesar 3,778563, rata-rata dari kuadrat selisih antara nilai prediksi dan nilai aktual atau MSE sebesar 21,661444 dan rata-rata persentase kesalahan absolut antara nilai prediksi dan nilai aktual atau MAPE sebesar 12%. Berdasarkan nilai MAPE yang diperoleh, dapat disimpulkan bahwa prediksi penjualan untuk penjualan produk ini dapat dikategorikan Baik. Hasil penelitian ini dapat dijadikan rekomendasi untuk memprediksi kisaran jumlah penjualan produk diperiode yang akan datang agar menyesuaikan persediaan stok, anggaran dan jadwal produksi.

Bantuan modal diperlukan untuk meningkatkan produktivitas dan mendukung pertumbuhan industri kecil dan menengah (IKM). Dinas Perindustrian dan Tenaga Kerja Kabupaten Bangkalan seringkali mengalami kesulitan untuk merekomendasikan dan memprioritaskan penerima bantuan dari IKM agro dan non-agro sesuai dengan kriteria yang telah ditetapkan. Permasalahan tersebut mengakibatkan terjadinya penyaluran bantuan modal yang tidak efisien. Sistem rekomendasi untuk penentuan prioritas bantuan IKM dengan menggunakan metode AHP dan MOORA. Metode AHP digunakan untuk mengatribusikan nilai bobot pada kriteria yang berbeda, sedangkan metode MOORA dimanfaatkan untuk melakukan peringkat pada setiap opsi yang ada. Parameter kriteria yang digunakan adalah jumlah produksi, tenaga kerja, omzet dan surat ijin usaha. Pengujian validasi metode MOORA mendapatkan nilai precision 86%, recall 90%, accuracy 80% dan F1-score 88% yang menunjukkan metode MOORA mampu menghasilkan rekomendasi alternatif yang terbaik. Manfaat hasil penelitian ini dapat membantu penyalur untuk merekomendasikan dan memprioritaskan penerima bantuan modal IKM sesuai dengan kriteria.

Historical education is essential in forming human character because it is the basis for forming a sense of nationalism. Along with the development of technology, learning models are also affected. Learning can be facilitated through learning sources other than educators, thereby changing the role of educators in learning and adapting learning to current developments. One of the learning resources is game technology. Game applications can be a learning medium because of their visual and interactive nature. Packing history learning into an educational game application might be an alternative source of learning that is interesting and not monotonous. The purpose of this research is developing education media as an Android-based historical learning tool by packaging the historical learning of the story of Sunan Kalijaga in the form of a game application. The results of the black box, white box trials that went well, and the feasibility test that was carried out on 15 respondents, 93% stated that the material presented was appropriate so that it can be concluded that the game application is suitable for use as an alternative media for mobile learning by teachers and students with an assessment feature using an Android smartphone. Abstrak Pendidikan sejarah sangat penting dalam proses pembentukan karakter manusia karena merupakan dasar dari pembentukkan rasa nasionalisme. Seiring dengan berkembangnya teknologi, Pembelajaran pun ikut terpengaruhi. Pembelajaran dapat dipermudah melalui sumber pembelajaran selain pendidik, sehingga menggubah peran pendidik dalam pembelajaran dan menyesuaikan pembelajaran dengan perkembangan zaman saat ini. Salah satu sumber pembelajaran itu adalah teknologi game. Aplikasi game dapat menjadi media pembelajaran karena sifatnya yang visual dan interaktif. Mengemas pembelajaran sejarah kedalam sebuah aplikasi game edukasi mungkin bisa menjadi salah satu alternatif sumber pembelajaran yang menarik dan tidak monoton. Tujuan dari penelitian ini adalah membuat aplikasi game “Lokajaya Sang Kalijaga” sebagai sarana pembelajaran sejarah berbasis Andoid dengan mengemas pembelajaran sejarah kisah Sunan Kalijaga ke dalam bentuk aplikasi game. Hasil dari uji coba black box, white box yang berjalan baik serta uji kelayakan yang telah dilakukan pada 15 responden, 93% menyatakan materi yang dibawakan telah sesuai sehingga dapat disimpulkan aplikasi game layak dipergunakan sebagai media alternatif pembelajaran secara mobile oleh guru dan murid dengan fitur penilaian menggunakan smartphone Android.

The COVID-19 pandemic has had a major impact on many things. Education, which is one of the important aspects in supporting human life, also felt the impact of the pandemic. Based on Circular Letter Number 4 of 2020 concerning the Implementation of Education Policies during the emergency period of the spread of COVID-19, the government has a policy that face-to-face education is not allowed to be carried out. Therefore, the world of education began to implement distance learning or e-Learning. In addition to how to teach and learn, there are other things that need to be considered for the success of the process. Another thing is student learning motivation. Changes in the teaching and learning process also have an impact on student learning motivation. In this study, a questionnaire has been distributed to find out the core drive of student learning motivation. Based on a questionnaire that has been filled out by 167 respondents, the core drive that influences students' learning motivation is at a high level. This indicates that the level of student learning motivation during the COVID-19 pandemic is still high.

Choosing the most suitable day-old chick (DOC) broiler chicken supplier is currently one of the most important issues that must be addressed. This is because selecting the most suitable supplier can reduce the amount spent on purchases and the risk of sick chickens being delivered by the supplier. Another problem related to supplier selection that has been happening so far is the quality of products that are not following company standards or rejected products. The number of products provided does not match what was ordered by the company. The decision support system (DSS) can evaluate and select suppliers using multi-criteria characteristics related to the solutions offered based on parameters quality, price, delivery, supplier certificates, and death claims after the chickens have been delivered. The Analytical Hierarchy Process (AHP) and the Simple Additive Weighting (SAW) methods are used in this study as a comparison to produce the best-recommended accuracy value to get the best decision results based on ranking. The test results state that the AHP and SAW methods go well. The test was carried out using a dataset of the last ten months of history of purchasing docs broiler chicken from suppliers. The comparison of the results of the F1-score value between the AHP and SAW methods is 94% and 87%, respectively. The results state that the AHP method is superior as a system recommendation that can produce the best alternative supplier.

Currently, the competition of delivery service providers is increasing. Companies must strive to improve customer service and satisfaction to survive in this competition. One of the service improvements relates to the queuing system. This research was conducted at the counter of a logistics company, which provides delivery and expedition services, located in Cikarang, Bekasi. There was often a build-up of queues at counters and accumulated goods to be packaged. As a result, officers have very high utilization, because they should serve the customers; meanwhile, they also have to do packing works. This study uses a simulation approach to analyse and improve the performance of the current queuing system. Simulations are carried out using Promodel in the delivery handling system, namely starting when the customer enters the store, handling customer needs (including in the case of packing the goods until the service is complete and the customer leaves the store. There are two suggestions for improvement; short-term and medium-term improvement proposals for the short-term focus on customer service (excluding packaging), namely by providing registration forms and also preparing a refund that can shorten the service process. Meanwhile, the long-term improvement is to strengthen the service system with an additional information system, standardization of processes and costs, as well as extra officers who specialize in packing. The simulation results show that short and medium-term improvement can reduce waiting time and utilization, and increase the number of goods packed.

This study aims to select the best suppliers based on Fuzzy-AHP and ELECTRE methods in multinational automotive companies in Indonesia. Fuzzy AHP method is used to determine the weight of criteria and sub-criteria in reducing uncertainty and unclear information. Meanwhile, the ELECTRE method is used to rank alternative suppliers. Both methods are used to reduce subjective decisions and improve the accuracy of selected suppliers. After applying Fuzzy AHP and ELECTRE methods, the supplier chosen for the steel fuel tank is S1, for side beam impact is S2, wheel disc is S2, Seat is S1, door trim is S2, lamp is S3, headlining is S3, insulator is S1 and hose is S3. Keywords: JIT, supplier performance, Fuzzy-AHP, ELECTRE

Tofu industry dominates by small-scale business with traditional production methods. On the other hand, demand for tofu products is quite high, and it is needed to increase tofu production. This study discusses the making of hygiene tofu maker tools able to simplify the production process. Based on the results of interviews with three tofu home industries in Cikarang, Bekasi, 17 request attributes of making an assistive tool obtain. Based on this, four technical responses were collected and will be developed become four concept designs. After conducting concept selection, it is determined to plan for making a tool which can simplify the process of making tofu. That is, starting from the manufacturing process, which starts from the pulp to the process of making tofu, can be put together into one manufacture of the tool. Thus, designing this tool can integrate the six stages of the tofu production process.

Maintenance cost becomes one of the problems that manufacturing company is facing nowadays due to lack of maintenance system. The main objective of this research is to reduce the maintenance cost on auto rooting machine in toy manufacturer by developing a scheduled preventive maintenance. Data of machine breakdowns and costs related to maintenance, components, and the interval time of failure for each machine were collected. To develop a preventive maintenance system, the interval of component replacement must be determined. The minimum cost model is attained by finding the right interval time. The result of this research shows that by implementing proposed maintenance schedule the machine reliability has 45% increase and maintenance cost decreases by 48%.