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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Sistemasi: Jurnal Sistem Informasi Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Cyberspace: Jurnal Pendidikan Teknologi Informasi Rang Teknik Journal JMM (Jurnal Masyarakat Mandiri) J I M P - Jurnal Informatika Merdeka Pasuruan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat JOURNAL OF COMMUNITY MEDICINE AND PUBLIC HEALTH RESEARCH Community Empowerment Jurnal Teknimedia: Teknologi Informasi dan Multimedia EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Jurnal Locus Penelitian dan Pengabdian Bakti Sekawan : Jurnal Pengabdian Masyarakat JER Jurnal Penelitian Sains Teknologi
Helmi Imaduddin
Universitas Muhammadiyah Surakarta
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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RANCANG BANGUN SISTEM INFORMASI PENDAFTARAN EKSTRAKURIKULER BERBASIS WEBSITE (STUDI KASUS: SDN 06 NGRINGO) Anni'mah, Risyma Muti' Styandri; Imaduddin, Helmi
Rang Teknik Journal Vol 9, No 1 (2026): Vol. 9 No. 1 Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31869/rtj.v9i1.6970

Abstract

Extracurricular activities are essential for cultivating pupils' potential in primary education. SDN 06 Ngringo is dedicated to providing a range of extracurricular programs to develop students' skills, interests, and talents. The existing manual registration method results in inefficiencies, data management challenges, restricted access to information, and diminished openness. This study intends to build and implement a web-based system for extracurricular registration information at SDN 06 Ngringo. The employed methodology is the Waterfall model, encompassing requirement analysis, design, coding, testing, and maintenance. The system is constructed utilizing HTML, PHP, Bootstrap, the Laravel framework, and MySQL as the database. This technology is anticipated to streamline and expedite the registration process, enhancing the efficiency and efficacy of extracurricular activity management. This research is expected to positively enhance students' learning experiences and facilitate the optimal development of their potential at SDN 06 Ngringo.
Early Classification of Diabetes Risk in Productive Age Groups Using Machine Learning Zaki, Muhammad Ghifari; Imaduddin, Helmi
Jurnal Penelitian Sains Teknologi Vol. 2, No. 1, March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i1.16430

Abstract

This research aims to develop an early detection classification model for diabetes risk among the productive age group (18–44 years) using a machine learning approach. Implementing the CRISP-DM methodology, this study utilized the Diabetes Health Indicators Dataset from CDC BRFSS 2015, which was refined to 48,867 observations. The class imbalance issue (4.51% diabetes positive) was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) to achieve a 1:1 class ratio in the training set. Elbow curve analysis and mutual information identified 10 optimal features that balance model performance and system usability. Three algorithms were evaluated Logistic Regression, Random Forest, and XGBoost and validated using Stratified 5-Fold Cross-Validation. The results demonstrate that Logistic Regression achieved the best performance for health screening purposes with a recall of 75.06% and ROC-AUC of 83.62%, capable of detecting three out of four diabetes cases with high consistency (cross-validation: recall 75.02% ± 2.35%). This model proved to be the most effective early screening tool for diabetes risk, supporting early detection and medical intervention for the productive age population.
RANCANG BANGUN SISTEM INFORMASI PERSEDIAAN STOK BARANG BERBASIS WEBSITE (STUDI KASUS: PT. PERDANA SAKTI INDONESIA) Sannia Putri Ariansyah; Helmi Imaduddin
Rang Teknik Journal Vol 9, No 1 (2026): Vol. 9 No. 1 Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31869/rtj.v9i1.6967

Abstract

In the contemporary age of digital transformation, where the necessity for efficiency and precision is critical, numerous firms encounter substantial challenges in properly managing their inventory systems, particularly in the context of rising product demand and operational intricacy. PT. Perdana Sakti Indonesia faces significant challenges due to its reliance on manual data processing methods, resulting in data entry errors, delays in stock information updates, and protracted calculation processes that impede productivity and responsiveness. The deficiencies in inventory management jeopardize financial stability and reduce consumer happiness, particularly when products are out of stock or delivery schedules are disrupted. This project seeks to design and create a complete web-based inventory information system utilizing the Waterfall methodology as a systematic software development framework. The system is built with Laravel as the primary framework, PHP for backend development, MySQL as the database engine, and HTML/CSS for designing a user-friendly interface. The use of automation and real-time monitoring is anticipated to markedly boost data accuracy, minimize human errors, optimize stock management, and improve overall operational efficiency. The use of this system is anticipated to enhance PT. Perdana Sakti Indonesia's inventory management and service quality.
A robust model for early detection of chronic kidney disease leveraging machine learning and data balancing techniques Helmi Imaduddin; Siti Agrippina Alodia Yusuf; Muhammad Syahriandi Adhantoro
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11247

Abstract

Chronic kidney disease (CKD) requires reliable early screening, yet clinical datasets are often highly class imbalanced, which can bias machine learning models and reduce detection quality. This study presents a unified evaluation of two imbalance mitigation strategies, synthetic minority over-sampling technique (SMOTE), and cost-sensitive learning, across six classifiers: decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Experiments were conducted on a public CKD dataset with 1,659 records and 54 features using a consistent pipeline including preprocessing, feature selection, imbalance handling, and stratified k-fold cross-validation. Models were assessed with accuracy, precision, recall, and F1-score. Results show that the imbalance strategy materially changes model behavior: cost-sensitive learning generally improves precision, while SMOTE more often increases recall and F1-score. The best overall performance was achieved by XGBoost with cost-sensitive learning, reaching 93% accuracy and 92% precision, outperforming prior reports on the same dataset. RF remained stable across both strategies, whereas KNN was sensitive to SMOTE induced distribution shifts. These findings provide practical guidance for selecting imbalance handling methods to improve healthcare machine learning for CKD detection.
Implementasi Arsitektur CNN untuk Klasifikasi dan Identifikasi Penyakit Daun Tanaman Padi Helmy Purnomo Hidayat; Helmi Imaduddin
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9192

Abstract

Leaf diseases in rice plants are a serious threat that can reduce productivity and crop quality, thus directly impacting national food security. Farmers still face various obstacles in identifying diseases conventionally, especially in the early stages of infection which can potentially cause delays in treatment. This study aims to develop a Deep Learning-based rice leaf disease classification system by building a Convolutional Neural Network (CNN) architecture independently (from scratch). The dataset used includes 18,445 rice leaf images categorized into ten disease classes, with an allocation of 70% training data, 15% validation data, and 15% test data. All images were resized to 224×224 pixels before being input into the model. Data augmentation was applied to prevent overfitting by rotation (20°), horizontal and vertical shifts (15%), shear (15%), zoom (15%), horizontal flip, and brightness variations (0.8-1.2). The CNN model was designed using five convolution blocks with cascaded filter configurations (32, 64, 128, 256, 512) using a 3×3 kernel and equipped with Batch Normalization, MaxPooling2D, and Dropout. The model was compiled using the Adam optimizer with a learning rate of 0.0001, a categorical cross-entropy loss function, and ReLU and Softmax activation functions. The training process used a batch size of 8 equipped with EarlyStopping and ReduceLROnPlateau callbacks. The experimental results showed that training with 75 epochs produced optimal performance with an accuracy of 97.91%, a precision of 0.9792, a recall of 0.9791, and an F1-score of 0.9790 on the test data. Evaluation per class showed that the Bacterial Leaf Blight and Tungro classes achieved perfect accuracy (100%), while Leaf Blast had the lowest accuracy (93.8%) due to its visual similarity to Brown Spot. The best model was implemented into a web system called Pariku using the Flask framework, which provides automatic diagnosis features, prediction confidence levels, and Integrated Pest Management (IPM)-based treatment recommendations.
Implementasi Metode Random Forest Untuk Memprediksi Jumlah Penjualan Gorden Berdasarkan Data Historis Amiladito Adhyatma Wijanarko; Helmi Imaduddin
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9194

Abstract

The rapid development of information technology has encouraged companies, including Tova Gorden, a small business engaged in curtain sales, to adopt technology to improve operational efficiency and competitiveness. Tova Gorden often faces obstacles in fulfilling orders, especially when demand suddenly increases, which is exacerbated by limited stock, raw material difficulties (such as smokers), fabric pre-order systems, and time-consuming production processes. Determining stock that is still based on employee estimates often leads to inefficiencies in the form of shortages or excesses of goods. This condition highlights the urgent need for an accurate prediction system to optimize inventory management. This study aims to implement and test the performance of the Random Forest algorithm, which is an ensemble learning method, to predict the number of curtain sales based on historical sales data. The collected data includes historical information related to curtain sales, including sales weeks, curtain motifs, and sales volumes. Unlike previous studies that generally use Linear Regression and focus on products with stable sales patterns, this study applies Random Forest to address more fluctuating curtain demand patterns. This research method includes several stages, namely Data Collection, Exploratory Data Analysis (EDA), Data Preprocessing, Data Splitting (70% training, 15% validation, 15% testing), Modeling with Random Forest, Evaluation, and Deployment. The evaluation results show that the model has excellent performance, with a coefficient of determination (R²) value of 97.83% on training data, 93.72% on validation data, and 96.64% on test data. Furthermore, the model is integrated into a web-based system using the Flask framework. This system is equipped with data upload features, prediction processes and curtain category grouping, and presentation of model evaluation results.
Detecting Muslim Students Mental Health with an Islamic Educational Approach using Machine Learning Taftazani Ghazi Pratama; Toni Ardi Rafsanjani; Riana Putri Rahmawati; Helmi Imaduddin
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5732

Abstract

Mental health among university students has become a major concern in higher education, particularly in the post-pandemic era, which has left students facing various academic, social, and psychological pressures. Unfortunately, efforts for early detection of mental health issues on campus remain limited, especially in the context of Muslim students who live within an Islamic cultural framework. This study offers an innovative approach by integrating advanced machine learning technology with the depth of Islamic educational values to develop an early detection system that is not only accurate but also humanistic and contextually relevant. The dataset for this study was obtained through a survey of 127 students at Universitas Muhammadiyah Kudus, including variables related to psychological conditions and the intensity of religious practices, used to detect whether students experience mental health problems or maintain good mental health. The research methodology includes data collection, preprocessing, feature analysis, model development using classification algorithms such as Random Forest, SVM, KNN, and Decision Tree, model performance optimization using GridSearchCV, and evaluation. Evaluation of the four models indicated that prior to optimization, SVM and KNN achieved the best performance, both with an accuracy of 88.46%. After optimization with GridSearchCV, SVM became the top-performing model, achieving an accuracy improvement of more than 5%, reaching 94.05%. Feature analysis revealed that levels of anxiety, fatigue, and religious practices such as prayer and dhikr were the primary determinants in mapping students’ mental health conditions. These findings suggest that Islamic values such as tawakkul (trust in God), sabr (patience), and syukur (gratitude) are not merely theological concepts but can also serve as scientific instruments, converted into predictive features in data-driven technologies. This study demonstrates that an SVM model optimized with GridSearchCV is effective in detecting university students’ mental health and has the potential to serve as an early warning system in Islamic campus settings.
Rancang Bangun Sistem Informasi Toko Online Yennyshop Berbasis Website Menggunakan Framework Laravel Carissa Aprilia Pranowo; Helmi Imaduddin
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6117

Abstract

Penelitian ini membahas tentang perancangan dan pengembangan sistem informasi toko online YennyShop. Toko Yennyshop menghadapi permasalahan dalam efisiensi penjualan produk secara manual, keterbatasan aksesibilitas pembeli, dan kurangnya manajemen data yang efektif. Ada penelitian ini yang bertujuan untuk meningkatkan efisiensi dan efektivitas penjualan produk dengan menggunakan situs web sebagai sarana pemasaran produk. Sistem toko online yennyshop dirancang untuk mengatasi permasalahan tersebut dengan menyediakan platform yang memungkinkan pembeli untuk melakukan transaksi secara online, memperluas jangkauan pasar dan meningkatkan pengelolaan data penjualan. Penelitian ini menggunakan metode waterfall dalam proses pengembangan sistem, yang meliputi analisis kebutuhan, desain, pemrosesan, pengujian, dan pemeliharaan. Pengembangan sistem menggunakan bahasa pemrograman PHP dengan framework Laravel , pengujian fungsional sistem dengan metode black box testing , dan System Usability Scale (SUS ), serta pemeliharaan rutin untuk menjaga kinerja sistem. Hasil penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem informasi toko online yang efektif dan efisien, serta meningkatkan pengalaman pembeli dalam melakukan transaksi secara online
Co-Authors Afiana Azizah, Synta Nur Ahwy Oktradiksa Akhmad Liana Amrul Haq Ali Zainal Abidin Amiladito Adhyatma Wijanarko Amrina Rosyada Anjarwati, Jesika Anni'mah, Risyma Muti' Styandri Anton Agus Setyawan Arda Pratama Putra Chafid Astriani, Anggit Azizah Fatmawati Bintang, Yayes Kasnanda Brian Aditya Hermansyah Brian Aditya Hermansyah Carissa Aprilia Pranowo Dedi Gunawan Devi Afriyantari Puspa Putri Dewi Sasika Rani Dewita Puspawati Diah Priyawati Dias Aziz Pramudita Dimas Aryo Anggoro Fatah Yasin Al Irsyadi Fiddin Yusfida Ala Frischa Aura Salsabilla B Hayati, Luthfiana Nur Helmy Purnomo Hidayat Ihsan Cahyo Utomo Khoirunnisa Shafira Deshpande Kirana, Vintan Muhammad Arkan Setyawan Muhammad Halim Maimun Muhammad Mutawadhi’ Alfajri Muhammad Randhy Kurniawan Muhammad Syahriandi Adhantoro Muslihah , Isnawati Nabil Aziz Bima Anggita Nabila Annisa Harum Nani Sulistianingsih Novel Idris Abas Praswati , Aflit Nuryulia Retno Asih Setyoningrum Riana Putri Rahmawati Rika Hapsari Romadhon, Anita Lusi Sakina, Alivia Rahma Sania Citra Palupi Sannia Putri Ariansyah Taftazani Ghazi Pratama Toni Ardi Rafsanjani Widi Widayat Yusuf, Siti Agrippina Alodia Zaki, Muhammad Ghifari