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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) International Journal of Artificial Intelligence Research Jurnal Komtika (Komputasi dan Informatika) Jurnal Consulenza : Jurnal Bimbingan Konseling dan Psikologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Jurnal Komtika (Komputasi dan Informatika)
Uky Yudatama, Uky
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Journal of Computer System and Informatics (JoSYC)

 1 
Prediksi Bahan Baku Kerupuk Rambak UMKM Tiga Berlian dengan Single Moving Average Sebastian, Devara Avila; Yudatama, Uky; Primadewi, Ardhin
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5604

Abstract

The "Tiga Berlian" rambak cracker MSME is an MSME in the snack food sector located in Banyuwangi Bandongan Village which produces crackers made from cowhide. Rambak crackers are a superior product with total demand per month reaching 150 kg. Tiga Berlian UMKM has difficulty in predicting and calculating raw material supplies for the coming period so excess raw material stock often occurs, such as cowhide or buffalo as the main raw material that is wasted due to uncertain demand. Single Moving Average (SMA) is used as a prediction method for raw materials in Tiga Berlian. Calculations with several periods (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11) will be tested for accuracy using MAD, MSE and MAPE. The 11 predictions made with Tiga Berlian raw material data for 12 months, the 11th-period calculation resulted in a MAD value of 2.56198, a MAPE value of 2.561983, and an MSE value of 72.20135 as the smallest and most relevant accuracy value.
Peramalan Stok Penjualan Sahabi Frozen Food dengan Weighted Moving Average Karim, Muhammad Abdul; Yudatama, Uky; Primadewi, Ardhin
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5824

Abstract

Jajan Sahabi Frozen Food (Jajan Sahabi) is a small and medium-sized enterprise (SME) located in Secang, Magelang, offering various types of frozen foods such as burgers, meatballs, and fries from multiple brands. Currently, inventory tracking at Jajan Sahabi is still conducted manually using a logbook, while stock replenishment is carried out weekly based on the remaining inventory. This approach leads to stock imbalances, either resulting in overstock, which may cause losses, or understock, which could lead to customer dissatisfaction. Forecasting is employed as an alternative decision-making tool to predict product demand based on historical data over a certain period. This study analyzes meatball sales reports from January 2022 to December 2022 using the Weighted Moving Average (WMA) method. The accuracy of the forecasts was evaluated using Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The forecast results for January 2023 indicate that Meatball Ori is expected to sell 216.3 pieces with a MAD of 76.81 and a MAPE of 53.90%. Meatball Kanzler showed the best performance with a MAPE of 28.80% and a MAD of 28.53. In contrast, Meatball Ikan, Meatball Ikan TL, Meatball Fish dan Shrimp, Meatball Cheese Kanzler, and Meatball Wahid exhibited lower accuracy, with MAPE values ranging from 29.03% to 53.90%. These results suggest that the WMA method produces varying outcomes depending on the product, indicating that further adjustments may be necessary to enhance accuracy.
Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web Jalaluddin, Alif; Arumi, Endah Ratna; Sasongko, Dimas; Pinilih, Sambodo Sriadi; Yudatama, Uky; Arif Yudianto, Muhammad Resa
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4830

Abstract

Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.
Co-Authors AGUS PURWANTO Anggraini, Novi Kusuma Anwar, Tulkhah Mubasyir Ardhin Primadewi Auliya Burhanuddin Bambang Pujiarto Dimas Sasongko Emilya Ully Artha Endah Ratna Arumi, Endah Ratna Erlinda, Reza Ena Fatmala, Fungki Fendy Suhariadi Ghani, Mohd Khanapi Abd Harmadji, Dwi Ekasari Hasani, Rofi Abul Jalaluddin, Alif Kasih, Ginanjar Prianggoro Mahfuzah, S.N Maimunah, Maimunah Muhammad Abdul Karim, Muhammad Abdul Muhammad Resa Arif Yudianto Nindhy Prastiwi, Maulidina Nugroho Agung Prabowo Pamungkas, Eko Joko Prabowo, N Agung Purwono Hendradi R Arri Widyanto Sambodo Sriadi Pinilih Sebastian, Devara Avila Solikhin Solikhin Sugiarti, Rini Sukmasetya, Pristi Utari, Sudi Yudianto, Resa Arif Yulfikar, Muhammad Yulfikar, Muhammad