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All Journal Jurnal Simantec Jurnal SOLMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Vocatech : Vocational Education and Technology Journal Generation Journal Indonesian Community Journal Eduvest - Journal of Universal Studies Jurnal Informatika Polinema (JIP) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika INOVTEK Polbeng - Seri Informatika Almantiq
Jauhar Vikri, Muhammad
Universitas Nahdlatul Ulama Sunan Giri
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Optimizing Gated Recurrent Unit (GRU) for Gold Price Prediction: Hyperparameter Tuning and Model Evaluation on Historical XAU/USD Data Faqih, Abdul; Vikri, Muhammad Jauhar; Sa’ida, Ita Aristia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2352

Abstract

This study investigates the use of a Gated Recurrent Unit (GRU) model with a four-layer architecture for daily gold price closing prediction, motivated by the model's ability to effectively capture temporal dependencies in time series data. Gold price forecasting is highly challenging due to its volatility and external factors, making it an important area of research for investors and financial analysts. By systematically optimizing hyperparameters through 72 combinations of epochs, batch size, GRU layer units, and dropout rates, the study identifies the optimal configuration (100 epochs, batch size of 16, 256 units, dropout rate 0.1) based on MSE performance on validation data. The best model achieved MAE of 25.76, MSE of 954.97, and RMSE of 30.90, after inverse transformation on test data. These results highlight the potential of the GRU model in accurately forecasting gold prices, with implications for financial decision-making . However, the prediction error suggests that further improvements could be made by incorporating external factors or exploring advanced model architectures.
Pemberdayaan Masyarakat Desa Sranak Melalui Produksi dan Pemasaran Produk UMKM Berbasis Digital Rohmah, Roihatur; Vikri, Muhammad Jauhar; Alawi, Zakki; Nugraha, Krisna Wahyu; Aralia, Shafa Kirana; Bassalam, Ahmad Nasih Ulwan
Jurnal SOLMA Vol. 14 No. 2 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i2.18974

Abstract

Pendahuluan: Pemberdayaan masyarakat merupakan strategi penting untuk meningkatkan kemandirian dan kesejahteraan komunitas, terutama di pedesaan. Desa Sranak, Kecamatan Trucuk, Kabupaten Bojonegoro, memiliki potensi dalam sektor Usaha Mikro, Kecil, dan Menengah (UMKM). Studi ini bertujuan untuk meningkatkan keterampilan masyarakat dalam produksi dan pemasaran produk UMKM berbasis digital. Metode: Kegiatan ini berfokus pada pelatihan pemasaran digital dan penerapannya dalam penggunaan Whatsapp Business. Hasil: Adanya peningkatan pengetahuan sekitar 90% tentang Whatsapp Business dan 81% sudah mempraktikannya. Kesimpulan: Kegiatan ini bermanfaat karena Masyarakat dapat melakukan pemasaran digital untuk mengembangkan produk UMKM mereka.
Sistem Pendukung Keputusan Pemilihan Jenis Investasi Menggunakan Metode Analytical Hierarchy Process (AHP) Fauzi, Muhammad; Vikri, Muhammad Jauhar; Wahyudhi, Sunu
Multidisciplinary Applications of Quantum Information Science (Al-Mantiq) Vol. 4 No. 1 (2024): Multidisciplinary Applications of Quantum Information Science (Al-Mantiq)
Publisher : Al-Mantiq

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/almantiq.v4i1.2183

Abstract

Currently, the investment sector is experiencing rapid development in Indonesia. However, the lack of understanding of how to choose the optimal investment results in less accurate and appropriate decision making, which in turn can result in losses in making investments. The purpose of the investment type selection decision support system is to provide convenience to new investors in making decisions regarding the type of investment. Dataxcollection is done by interview, observationcand literature study. Fromxthe results of data collection, several alternatives were obtained, namely stocks, mutual funds, bonds and deposits with selection criteria including risk, return and liquidity. Data processing is done with one of the DSS (Decision Support Systems) methods, namely the AnalyticalxHierarchy Process (AHP) method, with the results of stocks being determined as the best investment option.
Comparison of Decision Tree Algorithms and Support Vector Machine (SVM) In Depression Classification In Students Risqi, M. Khoirul; Dwi Prastya, Ifnu Wisma; Vikri, Muhammad Jauhar
Eduvest - Journal of Universal Studies Vol. 5 No. 4 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i4.51108

Abstract

Mental health in adolescents, especially students, is an important concern in the world of education. Early detection of symptoms of depression in students can help preventive efforts in handling them. This study aims to compare the performance of two classification algorithms, namely Decision Tree and Support Vector Machine (SVM) in detecting the level of depression in students based on data obtained from the Kaggle platform. The dataset used consisted of 502 student data with 10 features that caused depression and 1 target class. The research stage includes data preprocessing, which includes data cleaning, categorical value encoding, and normalization with the Min-Max Scaling method. The model was developed using the 5-Fold Cross Validation method to evaluate the classification performance of each algorithm. Model evaluation was carried out using precision, recall, and accuracy metrics. The test results showed that the SVM algorithm had better performance with a precision value of 93.63%, recall of 95.21%, accuracy of 94.22%, and F1-score of 94.68%. Meanwhile, Decision Tree obtained a precision of 81.77%, a recall of 84.90%, an accuracy of 82.86%, and an F1-score of 83.64%. Based on these results, it can be concluded that the Support Vector Machine is superior in classifying depression in students compared to Decision Tree
REAL-TIME TOMATO QUALITY DETECTION SYSTEM USING YOU ONLY LOOK ONCE (YOLOv7) ALGORITHM: Sistem Deteksi Mutu Tomat Secara Real-time Menggunakan Algoritma You Only Look Once (YOLOv7) Muarofah, Isna Ayu; Vikri, Muhammad Jauhar; Sa'ida, Ita Aristia
ULTIMATICS Vol 15 No 2 (2023): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v15i2.3337

Abstract

Real-time object detection is a crucial aspect of computer vision. With the increasing prominence of the big data field, it has become easier to gather data from various sources. Over the past few decades, computer vision inspection systems have become essential tools in agricultural operations, and their usage has seen a significant rise. Computer vision automation-based technology in agriculture is increasingly being employed to enhance productivity and efficiency. Tomato is a widely utilized crop commodity, finding applications in food, cosmetics, and pharmaceuticals. Consequently, tomato farming continues to evolve and has become one of the nation's export commodities. YOLO is an algorithm capable of real-time object detection and recognition. In this study, the YOLOv7-tiny architecture, which has lower computational overhead, was utilized. For quality detection of tomatoes, they were categorized into three classes: ripe, unripe, and defective. The trained model yielded a recall score of 0.97, precision of 1.0, a PR-curve of 0.838, and an F1-score of 0.81, indicating that the model learned effectively. The research achieved an accuracy of 90.6% on original images with an average IoU of 0.90 and a detection time of 2.7 seconds. In images with added light disturbance, the average accuracy was 91.2%. Images with reduced light yielded an average accuracy of 92%, while images with blur disturbance had an average accuracy of 78.2%. In real-time testing, ripe tomatoes were detected up to a maximum distance of 90cm, unripe tomatoes at 90cm, and defective tomatoes at 70cm.
Deteksi Kualitas Buah Sawo dengan Pendekatan Ekstraksi Fitur GLCM dan Algoritma Support Vector Machine Fidiya, Karisma Risma; Vikri, Muhammad Jauhar; Kartini, Alif Yuanita
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8519

Abstract

The quality of sapodilla fruit is a crucial factor in ensuring product standards and consumer satisfaction. This study aims to detect the quality of sapodilla fruit using the Gray Level Co-occurrence Matrix (GLCM) method for texture feature extraction and Support Vector Machine (SVM) as the classification algorithm. A dataset of sapodilla fruit images was collected and processed using data augmentation techniques to enhance image variation. Extracted features, including contrast, homogeneity, energy, and correlation, were used as input for the SVM model. The model was developed using a train-test split approach and evaluated based on accuracy, precision, recall, and F1-score. Experimental results show that the proposed method successfully classifies sapodilla fruit into three categories—raw, ripe, and damaged—with an accuracy of 85%. This model was implemented in a MATLAB-based Graphical User Interface (GUI), enabling users to automatically classify sapodilla quality easily and efficiently.
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Jauhar Vikri, Muhammad; Wisma Dwi Prastya, Ifnu; Pradema Sanjaya, Ucta; Agung Barata, Mula
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
Analisis Perbandingan Algoritma SVM, Logistic Regression, Naive Bayes, dan XGBoost Untuk Deteksi Fake News Umar Farid Al Faqihi; Afril Efan Pajri; Muhammad Jauhar Vikri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9492

Abstract

The rapid growth of digital technology and internet access has completely changed how information is shared, enabling content to spread quickly across various online platforms. However, these advancements have also made it easier for misleading or entirely fabricated news to circulate, posing serious risks to social stability, political environments, and public health. This study tackles this problem by employing several machine learning-based classification methods for analyzing textual data. Four algorithms Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Extreme Gradient Boosting (XGBoost) were applied to detect linguistic patterns that differentiate genuine news from fake content. A major contribution of this research is the creation of a custom dataset gathered directly from Indonesian online news portals, comprising a total of 4,909 entries. The evaluation results demonstrate exceptionally high accuracy across the models: 99.69% for SVM, 99.39% for LR, 99.29% for NB, and 99.19% for XGBoost. To verify reliability, each model was further evaluated using cross-validation, yielding average accuracy scores of 99.57% (SVM), 99.52% (LR), 99.44% (NB), and 99.49% (XGBoost). These findings confirm that all four classifiers are highly effective and well-suited for identifying fake news in text-based data.
Analisis Prediksi Resiko Perceraian Menggunakan Algoritma Random Forest dengan Optimasi Hyperparameter Random Search Nisa, Indah Khoirun; Vikri, Muhammad Jauhar; Diansyah, Denny nur
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9529

Abstract

Divorce is a social problem that continues to increase and has negative impacts on the psychological, social, and economic conditions of individuals and families. This study aims to build a divorce risk prediction model using the Random Forest algorithm with hyperparameter optimization using the Random Search method. The dataset was obtained from the Kaggle platform with 170 samples and 54 psychological-behavioral attributes of couples. The research stages included data preprocessing, dataset splitting (80:20), baseline model development, hyperparameter optimization with Random Search, and evaluation using accuracy, precision, recall, and AUC-ROC metrics. The results showed that the model achieved 94.12% accuracy on the test data with 97% recall that minimizes false negative risk. Hyperparameter optimization successfully improved the model's internal stability with a cross-validation average of 98.57%, although the test accuracy was equivalent to the baseline model. A gap of 4.45% between validation and test accuracy indicates potential overfitting, which is common in small datasets. Feature importance analysis revealed five dominant psychological factors: willingness to compromise, effective communication, conflict resolution, alignment of life values, and forgiveness ability. This research contributes to the development of an early detection system for divorce risk based on machine learning and provides an empirical basis for more targeted counseling interventions
Analisis Faktor Keberhasilan Penjualan Kerajinan Tangan menggunakan Decision Tree dengan Optimasi Grid Search Septiana, Nailus Saidah Anindia; Vikri, Muhammad Jauhar; Sa’ida, Ita Aristia
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9534

Abstract

This study is motivated by the limited ability of handicraft Micro, Small, and Medium Enterprises (MSMEs) to analyze the key factors influencing sales success on e-commerce platforms, despite the availability of historical transaction data. Previous studies generally applied classification algorithms without systematic hyperparameter optimization, potentially leading to suboptimal models and overfitting issues. To address this gap, this research proposes the implementation of a Decision Tree algorithm optimized using Grid search Cross-validation. The dataset was obtained from the Brazilian e-commerce platform (Olist Dataset), specifically the ‘artes’ category as a proxy for handicraft products, with an 80:20 split for training and testing data. The optimization process explored 576 parameter combinations to determine the best configuration. The optimized model achieved an accuracy of 97.61% with a simplified tree structure (max_depth=None), enhancing interpretability. Feature importance analysis product_height_cm as the most dominant factor (64.23%), followed by product_height_cm, product_width_cm, Freight_value, product_weight_g, and price. These findings demonstrate that the combination of Decision Tree and Grid search effectively produces an accurate and interpretable predictive model, providing strategic decision-making support for handicraft MSMEs in digital marketplaces.
Co-Authors Abda Abda Abdul Faqih Achmad  Afandi Afril Efan Pajri Agung Barata, Mula Ahmad Sahru Romadhon Alif Yuanita Kartini Andhi Setiawan Aralia, Shafa Kirana Ardyka Bayu Reovan Aries Alfian Prasetyo Bakti Putra Pamungkas Barata, Mula Bassalam, Ahmad Nasih Ulwan Diansyah, Denny nur Dwi Prastya, Ifnu Wisma Fidiya, Karisma Risma Fitri Damayanti Guruh Putro Dirgantoro Ita Aristia Sa'ida Karisma Risma Fidiya Kartini, Alif Yuanita Laily Ulfiyah Moh. Muhajir Muarofah, Isna Ayu Mula Agung Barata Nisa, Indah Khoirun Nugraha, Krisna Wahyu Pradema Sanjaya, Ucta Putri Liana Rheyna Anggri Setyani Risqi, M. Khoirul Rofi'i, Muhamad Roihatur Rohmah Septiana, Nailus Saidah Anindia Sunu Wahyudhi Teguh Pribadi Umar Farid Al Faqihi Vita Dwi Rahmawati Wisma Dwi Prastya, Ifnu Zakki Alawi