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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research
Fadil Indra Sanjaya
Universitas Teknologi Yogyakarta, Sleman
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Integrasi Model Algoritma Genetika dan Constraint Satisfaction Problem pada Optimasi Penjadwalan Shift Karyawan UMKM Kuliner Arya Firgi Syuhada; Rodhiyah Mardhiyyah; Fadil Indra Sanjaya
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.842

Abstract

Employee shift scheduling in the Micro, Small, and Medium Enterprises (MSMEs) sector is a complex problem because it must consider various aspects such as workforce availability, work hour restrictions, and individual preferences. At the Nasi Balap Cucun MSME which operates in the culinary field, the challenge is even greater because most of its employees are active students with diverse class schedules. The scheduling process is still done manually often takes a long time and results in an unbalanced division of labor. To overcome this, this study developed an automatic scheduling system based on Genetic Algorithms combined with Constraint Satisfaction Problems (CSP). The system was built using the Python programming language with the DEAP library, considering shift needs, employee schedule requests, and operational constraints. The implementation results show that the system is able to generate efficient weekly schedules with an increase in time efficiency of up to 80%. After testing the system, it was found that the scheduling results would appear less than 10 seconds after the user generated the schedule. In addition, the system showed an increase in fitness value from -1000 in the initial generation to 54 in the 50th generation, which means this system is able to reduce potential conflicts in scheduling. This approach can be an effective solution for MSMEs in optimizing human resource management intelligently.
Pengembangan Sistem Prediksi Saham Menggunakan Model Hybrid Gated Recurrent Unit–Long Short-Term Memory Berbasis Integrasi Indikator Teknikal Konvensional Fajar Hanggoro Dwi Aryanto; Rr. Hajar Puji Sejati; Fadil Indra Sanjaya
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8988

Abstract

Stock price prediction is a crucial aspect of investment decision-making in the Indonesian capital market. This study aims to design a hybrid Gated Recurrent Unit–Long Short-Term Memory (GRU–LSTM) model architecture integrated with technical indicators such as Moving Average Convergence Divergence, Moving Average, Exponential Moving Average, and Relative Strength Index to improve the accuracy and objectivity of predictions. Additionally, this study aims to optimize model performance through grid search and implement it into a Flask-based web application as a decision support system for investors. The system was developed using a research and development approach at the Yogyakarta University of Technology. Historical data on PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI.JK) shares for the period from January 2, 2020, to October 17, 2025, was obtained through the Yahoo Finance API as the main dataset. The model was optimized to determine the best combination of hyperparameters. Evaluation was performed using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The test results show that the model achieved MAE 0.0241, MSE 0.0012, RMSE 0.0346, and MAPE 2.7%, indicating a high level of accuracy. The web application provides interactive visualization dashboard features, model development, and educational documentation. These findings confirm that the integration of deep learning with technical indicators is an effective solution for more measurable and systematic stock analysis.
Penerapan Metode Fuzzy Tsukamoto Dalam Sistem Pakar Diagnosis Penyakit Pada Sapi Jennie Nadia Baraputri; Fadil Indra Sanjaya
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6568

Abstract

Cattle are one of the most widely cultivated animals in Indonesia. Cattle diseases are also a major challenge in animal husbandry and can affect productivity and animal welfare. To face these challenges, an accurate disease detection and diagnosis system is needed. Such a system is essential to reduce the risk of disease spread and speed up the treatment process. Research was conducted to develop an expert system using the Fuzzy Tsukamoto method. This method was chosen because it can handle data uncertainty in clinical symptoms. To determine the diagnosis results, the system consists of five main stages, namely data collection of disease symptoms and characteristics, data fuzzification, rule base formation, fuzzy inference process, and defuzzification. The system is also designed by including symptom and characteristic variables, as well as diagnostic rules that help the diagnosis process automatically. Based on the fuzzy inference and defuzzification process that has been carried out, the final result for the diagnosis of Herpes disease is 90% and Mastitis disease is 90% which means the severity of the disease is “Severe”.
Co-Authors Arya Firgi Syuhada Fajar Hanggoro Dwi Aryanto Jennie Nadia Baraputri Rodhiyah Mardhiyyah Rr. Hajar Puji Sejati