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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Faizin, Arif
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Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Forecasting Harga Saham PT. ABCD Menggunakan Algoritma Fuzzy Time Series Khaq, Muchamad Izzul; Faizin, Arif; Havy, Ahmad Zulham Fahamsyah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 1 (2026): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i1.1639

Abstract

High stock price fluctuations pose a significant challenge for investors and analysts in determining investment strategies. Price dynamics influenced by economic, political, and psychological market factors require forecasting methods that can accommodate uncertainty and non-linear historical data patterns. This study applies Cheng's Fuzzy Time Series algorithm to predict the stock price of PT. ABCD by going through the stages of universe set formation, interval determination, fuzzification, fuzzy logic relationship formation, and defuzzification to obtain prediction results. The method implementation was carried out using two approaches: manual calculation using Microsoft Excel and automatic calculation using the Orange application. The results show that Cheng's method is able to produce predictions very close to the actual value, with an accuracy level measured using the Mean Absolute Percentage Error (MAPE) indicator of 0.058787% on both platforms. The consistency of the results between Excel and Orange proves the reliability of Cheng's method, so it can be used as a reference in supporting investment decision-making in the Indonesian capital market.
Optimization of the Naive Bayes Algorithm with SMOTETomek Combination for Imbalance Class Fraud Detection Arsanto, Arief Tri; Faizin, Arif; lutfi, Moch; Saadah, Zulfatun Nikmatus
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4719

Abstract

The use of credit cards in the modern era is increasing. Therefore, it is necessary to prevent it with the use of technology such as address verification systems (AVS), card verification methods (CVM), and personal identification Numbers (PIN). Dataset analysis needs to be carried out to analyze the history of transactions that have been carried out. In the fraud detection dataset, it can be seen that there are attributes that cause data imbalance. Class imbalance in a dataset is a significant problem in machine learning that can affect overall model performance. The number of majority samples is more significant in one class than the number of minority classes. This research used an oversampling approach using a combination of smote and tomek-link. The focus of this research is card fraud classification. Detection of imbalanced datasets or imbalanced classes is carried out using the Naive Bayes method as a classification algorithm. In addition, a combination of resampling techniques is also applied to overcome imbalanced classes in this dataset through the SMOTETomek approach. SMOTETomek is a method that reduces the number of samples by considering two adjacent data from the minority and majority classes. Meanwhile, from the problems above, the results of the performance of Naïve Bayes, which experienced issues with data imbalance in this study, a resampling method was proposed in the hope of improving the performance of the Naïve Bayes algorithm and in the results of the AUC ROC curve, the SMOTETomek method could improve the performance of the Naïve Bayes algorithm. The higher the ROC score. -AUC, the better the model performance in terms of its ability to differentiate between two classes, but the accuracy results do not experience a significant change.
Co-Authors Ahmad Zulham Fahamsyah Havy Arsanto, Arief Tri Khaq, Muchamad Izzul Lutfi, Moch Saadah, Zulfatun Nikmatus