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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 Community Empowerment Jurnal Teknimedia: Teknologi Informasi dan Multimedia EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Jurnal Locus Penelitian dan Pengabdian Bakti Sekawan : Jurnal Pengabdian Masyarakat
Helmi Imaduddin
Universitas Muhammadiyah Surakarta
Religion Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Mechanical Engineering Public Health Social Sciences Other

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Implementasi Metode Random Forest Untuk Memprediksi Jumlah Penjualan Gorden Berdasarkan Data Historis Wijanarko, Amiladito Adhyatma; Imaduddin, Helmi
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.
RANCANG BANGUN SISTEM INFORMASI PERSEDIAAN STOK BARANG BERBASIS WEBSITE (STUDI KASUS: PT. PERDANA SAKTI INDONESIA) Ariansyah, Sannia Putri; 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.v9i1.6967

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.
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.
Detecting Muslim Students Mental Health with an Islamic Educational Approach using Machine Learning Pratama, Taftazani Ghazi; Rafsanjani, Toni Ardi; Rahmawati, Riana Putri; Imaduddin, Helmi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): 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.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.
Co-Authors Afiana Azizah, Synta Nur Ahwy Oktradiksa Akhmad Liana Amrul Haq Ali Zainal Abidin Anjarwati, Jesika Anni'mah, Risyma Muti' Styandri Anton Agus Setyawan Ariansyah, Sannia Putri Astriani, Anggit Azizah Fatmawati Bintang, Yayes Kasnanda Brian Aditya Hermansyah Brian Aditya Hermansyah Dedi Gunawan Devi Afriyantari Puspa Putri Dewi Sasika Rani Diah Priyawati Dias Aziz Pramudita Dimas Aryo Anggoro Fatah Yasin Al Irsyadi Fiddin Yusfida Ala Frischa Aura Salsabilla B Hayati, Luthfiana Nur Hidayat, Helmy Purnomo Ihsan Cahyo Utomo Kirana, Vintan Maimun, Muhammad Halim Muhammad Arkan Setyawan Muhammad Mutawadhi’ Alfajri Muhammad Randhy Kurniawan Muslihah , Isnawati Nabil Aziz Bima Anggita Nani Sulistianingsih Novel Idris Abas Pranowo, Carissa Aprilia Rafsanjani, Toni Ardi Rahmawati, Riana Putri Romadhon, Anita Lusi Sakina, Alivia Rahma Sania Citra Palupi Taftazani Ghazi Pratama Widi Widayat Wijanarko, Amiladito Adhyatma Yusuf, Siti Agrippina Alodia