Article Per Year (5 Year)
p-Index From 2021 - 2026
2.619
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Journal of Information Technology and Computer Science (JOINTECS) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Transaction on Informatics and Data Science
Totok Chamidy
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Multi-Label Classification of Indonesian Qur'an Translation using Long Short-Term Memory Model Akbar, Ismail; Faisal, Muhammad; Chamidy, Totok
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1901

Abstract

Studying the Quran is an integral act of worship in Islam, necessitating a nuanced comprehension of its verses to ease learning and referencing. Recognizing the diverse thematic elements within each verse, this research pioneers in applying Deep Learning techniques, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), coupled with Word Embedding methods like Word2Vec and FastText, to refine the multi-label classification of the Quran's translations into Indonesian. Targeting core thematic categories such as Tawheed, Worship, Akhlaq, and History, the study aims to elevate classification accuracy, thereby enhancing the textual understanding and educational utility of the Quran's teachings. The employment of Bi-LSTM in conjunction with FastText and meticulous hyperparameter optimization has yielded promising results, achieving an accuracy of 71.63%, precision of 64.06%, recall of 63.60%, and a hamming loss of 36.17%. These outcomes represent a significant advancement in the computational analysis of religious texts, offering novel insights into the complex domain of Quranic studies. Furthermore, the research accentuates the critical role of selecting suitable word embedding techniques and the necessity of precise parameter adjustments to amplify model performance, thereby contributing to the broader field of religious text analysis and understanding. Through such computational approaches, this study not only fosters a deeper appreciation of the Quran's multifaceted teachings but also sets a new precedent for the interdisciplinary integration of Islamic studies and artificial intelligence.
Early Detection of Phishing Sites with Enhanced Neural Network Models Suarti, Isa; Chamidy, Totok; Crysdian, Cahyo
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.30068

Abstract

Phishing is a digital crime committed with the aim of obtaining personal data by creating a link or website that resembles the original. This form of cyber attack is caused by a notification in a text message, email, or phone call. A common anti-phishing countermeasure technique is to perform early detection of potentially phishing sites, primarily according to the source code features, which are required to traverse web page content, as well as third parties that slow down the process of clarifying phishing URLs. Although the latest technology has long been used in phishing early detection, there is still a need for manual feature engineering that is important and reliable enough to detect emerging phishing offenses. One of these involves training a neural network (NN) using a dataset of known phishing URLs and legitimate URLs. The research was conducted using 200 data, Data were separated into training and testing categories.  Training was done using 100 and 120 data. Training results on 100 data and 160 data had lower iterations and errors on the tanh activation function compared to the logistic activation function. The number of iterations that occur in logistic activation is as many as 400 iterations, while when using the tanh activation function only 175 iterations are needed.
Application of SMOTE in Sentiment Analysis of MyXL User Reviews on Google Play Store Badriyah, Badriyah; Chamidy, Totok; Suhartono, Suhartono
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.74-86

Abstract

Texts that express customer opinions about a product are important input for companies. Companies obtain valuable information from consumer perceptions of marketed products by conducting sentiment analysis. However, real-world text datasets are often unbalanced, causing the prediction results of classification algorithms to be biased towards the majority class and ignoring the minority class. This study analyzes the sentiment of MyXL user reviews on the Google Play Store by comparing the performance of the Logistic Regression and Support Vector Machine algorithms in the SMOTE implementation. This analysis uses TF-IDF to extract features and GridSearchCV to optimize the accuracy, precision, recall, and F1-score evaluation metrics. This study follows several scenarios of dividing training data and test data. SVM implementing SMOTE is the algorithm with the best performance using the division of training data (90%) and test data (10%), resulting in accuracy (73.00%), precision (67.13%), recall (65.82%), and F1-score (66.30%).
Prediksi Kualitas Udara Menggunakan Metode CatBoost Syukur, Mohamad Arif Abdul; Suhartono, Suhartono; Chamidy, Totok
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.249-258

Abstract

Air is essential for life, but industrial activities, forest fires, cigarette smoke, and transportation contribute to air pollution. AirVisual AQI 2024 data ranks Jakarta in 11th place globally, with the highest level of pollution, reaching 127, which is unhealthy for sensitive groups and poses a risk of causing serious illnesses, including skin and respiratory diseases. This research uses the CatBoost method to predict the air quality index using Jakarta SPKU data taken from Kaggle. The data is processed through pre-processing and divided into four models with different comparisons of training and testing data. Each model was tested with the parameters iteration, depth, learning rate, and l2_leaf_reg, using GridSearchCV to find the optimal combination. The results show that the model with 90% training data and 10% testing data provides the best accuracy of 97%, due to the larger proportion of training data. This research demonstrates that the CatBoost method can yield accurate air quality predictions, which is crucial in supporting efforts to mitigate the impact of pollution and enhance public health.
Spatial Decision Support System to Determine the Feasibility of Evacuation Posts in Natural Disasters Alviola, Nuril Afni; Almais, Agung Teguh Wibowo; Syauqi, A’la; Chamidy, Totok; A Basid, Puspa Miladin Nuraida Safitri; Anisa, Anisa; Wardana, M. Dafa
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.307-318

Abstract

This study aimed to improve the accuracy of determining the feasibility of evacuation posts after natural disasters using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within a Spatial Decision Support System (SDSS). A dataset of 50 evacuation posts from the 2021 Mount Semeru eruption was analyzed. The Rank Order Centroid (ROC) method was applied for criteria weighting, and TOPSIS was used to process the data. Results showed 72% accuracy, confirming that TOPSIS is a passable method for assessing post-feasibility based on accessibility, sanitation, and refugee facilities. Although the focus is on evaluating post-disaster evacuation posts, the system can be adapted for use in various other types of disasters. However, it is still dependent on historical data and lacks real-time adaptability. Future research can integrate Artificial Intelligence (AI) and Machine Learning (ML) with real-time data to improve decision-making in disaster management.
K-Means Clustering in Relevance Grouping of Undergraduate Informatics Jobs: Case Study at the Informatics Engineering Department, Universitas Muhammadiyah Malang, Malang, Indonesia Hariyanto, Dikky Cahyo; Harini, Sri; Chamidy, Totok
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12231

Abstract

Higher education is one of the levels of study expected to produce graduates competent in the field of knowledge taken. The large number of graduates from tertiary institutions with many job opportunities causes many graduates to work in ways that do not match their majors, so there is a need to evaluate the level of success of graduates learning achievements in tertiary institutions. This research aims to analyze data on the relevance of the work of undergraduate graduates in Informatics Engineering to what they have learned by the learning outcomes in the Informatics Engineering study program at the University of Muhammadiyah Malang using K-Means clustering. Using data from questionnaires measuring graduate learning outcomes and measuring job suitability for 137 respondents who had been tested for validity, reliability, and multicollinearity, the results of this research showed that the data was formed into three clusters with the analysis that 29.92% of UMM Informatics Engineering graduates were able to meet graduate learning outcomes and obtain jobs that are relevant to what they studied, 49.63% of other graduates also got jobs that were relevant to their major even though they lacked mastery of specific skills as measured by graduate learning outcomes, and 20.45% of other graduates got jobs that were less relevant to the field of Informatics engineering.
Analysis of Academic Information System Using Information System Success Model and System Quality Model Case Study of Institut Teknologi Nasional Malang setyowati, Kurnia Dwi; Chamidy, Totok; Faisal, Muhammad
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12234

Abstract

Advancements in information technology are progressing rapidly, providing numerous conveniences across various domains, ranging from personal needs to general activities. Academic Information System (SIAKAD) facilitates academic information management in educational institutions. The utilization of website-based SIAKAD is gaining popularity due to its ability to streamline access to information and minimize administrative errors. This Research aims to identify key factors contributing to the success of website-based SIAKAD at the National Institute of Technology Malang. Additionally, the Research seeks to assess the level of success of SIAKAD and evaluate the key factors that influence its success. This Research employed the Information System Success Model (ISSM) method and the System Quality Model (SQM). This study follows a seven-step workflow, from problem identification to conclusion, assessing SIAKAD ITN Malang's success using ISSM and SQM models. A survey of 100 respondents evaluated system quality, functionality, and user satisfaction. Related research highlights the importance of system quality in achieving positive user outcomes and overall success. Based on the research results, all hypotheses are accepted, indicating that Information Quality (IQ), System Quality (SQ), and Service Quality (SeQ) significantly influence Intention to Use (IU), User Satisfaction (US), and Net Benefit (NB) in the Use of SIAKAD at the National Institute of Technology Malang.
Multivariate LSTM for Drug Purchase Prediction in Pharmaceutical Management Brawijaya, Fanny; Almais, Agung Teguh Wibowo; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1313

Abstract

This study aims to develop a structured approach to predict the number of hospital drug purchases using deep learning techniques. The Multivariate Long Short-Term Memory (LSTM) model is designed to capture temporal and contextual patterns including transaction time, polyclinic type, and drug type to improve the efficiency of pharmaceutical management. The model was tested using outpatient transaction data at RSIA Fatimah Probolinggo hospital in East Java, Indonesia, through three configurations (A, B, and C) to determine the optimal parameters. The best model, the Model B1, produces a Mean Absolute Error (MAE) value of 10.239, Mean Absolute Percentage Error (MAPE) of 1.976%, and the Coefficient of Determination (R²) of 0.199, which indicates a high degree of accuracy. The results of the study prove that multivariate LSTM is able to model complex intervariable dependencies and provide superior results than conventional forecasting methods. In practical terms, this model can be used as a decision-making tool for hospital management in planning drug procurement, optimizing inventory, and preventing shortages and overstocks. The application of this model contributes to data-driven pharmaceutical supply chain planning in smart hospital management systems.
Stock Price Prediction Using Backpropagation ANN: Case Study of ADMR (2023–2025) Khozin, Muhammad; Abidin, Zainal; Chamidy, Totok
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1347

Abstract

This study develops an Artificial Neural Network (ANN) backpropagation model for predicting stock prices using ADMR stock data from 2023 to 2025, obtained from Yahoo Finance. Given the inherent volatility and unpredictability of stock prices, accurate forecasting plays a crucial role in investment decision-making. ANN models are particularly effective for capturing complex, non-linear relationships and patterns in financial data, which traditional statistical models may fail to address. In this research, various configurations were tested by adjusting the number of hidden neurons (5, 10, and 15) and learning rates (0.1, 0.3, and 0.5). The optimal model architecture was found to be 3-10-1, consisting of three input neurons, ten hidden neurons, and one output neuron, which achieved the best prediction performance with a Mean Absolute Percentage Error (MAPE) of 2.26%. This model was trained with a learning rate of 0.3 and completed in 915 iterations. However, the model's predictive capabilities are constrained by its reliance on historical stock prices alone, excluding external factors such as macroeconomic indicators, market sentiment, or trading volume, which may improve its generalization and overall accuracy. Future work could integrate these variables for better robustness and predictive power.
Co-Authors A Basid, Puspa Miladin Nuraida Safitri AA Sudharmawan, AA Adhitya Prayoga Permana Agung Teguh Wibowo Almais Ajib Hanani Akbar, Ismail Alviola, Nuril Afni Anisa Anisa Baehaqi Baskoro, Sentot Eko Brawijaya, Fanny Cahyo Crysdian Emha Ahdan Fahmi Elmuna Fresy Nugroho Hadziq Agasta, Hadziq Hariyanto, Dikky Cahyo Ismail Akbar Moh. Ainur Rohman Mohammad Faisal Fajar Fadilah Muhammad Faisal Muhammad Faisal Muhammad Khozin Ririen Kusumawati Saleh, Moh setyowati, Kurnia Dwi Sri Harini Suarti, Isa Suhartono Suhartono Suhartono Syahiduz Zaman Syauqi, A’la Syukur, Mohamad Arif Abdul Wardana, M. Dafa Zainal Abidin