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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF SCIENCE AND SOCIAL RESEARCH INFOKUM Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) Mejuajua Jurnal Minfo Polgan (JMP) Journal of Science Technology (JoSTec) Jurnal Pengabdian Masyarakat Nusantara Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Pengabdian kepada Masyarakat Journal of Technology and Computer (JOTECHCOM) JIPITI: Jurnal Pengabdian kepada Masyarakat International Journal of Educational Insights and Innovations (IJEDINS) Outline Journal of Community Development International Journal for Advanced Research
ruziq, fahmi
Battuta University
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DIABETES PREDICTION BASED ON MEDICAL RECORDS (PIMA INDIANS DIABETES DATASET) USING K-NN Ruziq, Fahmi; Wayahdi, M. Rhifky; Ginting, Subhan Hafiz Nanda
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.2981

Abstract

Abstract: The development of predictive technologies, especially artificial intelligence (AI) and machine learning, has opened up great opportunities in the health sector, including early detection of chronic diseases such as diabetes. This study aims to implement the K-Nearest Neighbors (KNN) algorithm in predicting the likelihood of a person having diabetes based on medical record data from the Pima Indians Diabetes Dataset. The dataset consists of 768 samples with eight key health features. The analysis process includes data cleaning, data distribution exploration, and data preparation for the modelling process. The distance between data is calculated using the Euclidean formula, and normalization is performed so that all features have equal weight. The data was then divided into training and test data with a ratio of 80:20. The analysis results showed an unbalanced class distribution, with more non-diabetic patients than those with diabetes. The age group of 21-30 years dominates in the dataset. The implementation of KNN in this study shows that the method is effective for medical classification based on numerical data. This research demonstrates the potential of KNN as a practical and easy-to-implement early diagnosis tool in data-driven health systems. Keyword: K-Nearest Neighbors, diabetes prediction, machine learning, medical data, classification. Abstrak: Perkembangan teknologi prediktif, khususnya kecerdasan buatan (AI) dan pembelajaran mesin (machine learning), telah membuka peluang besar dalam bidang kesehatan, termasuk deteksi dini penyakit kronis seperti diabetes. Penelitian ini bertujuan untuk mengimplementasikan algoritma K-Nearest Neighbors (KNN) dalam memprediksi kemungkinan seseorang menderita diabetes berdasarkan data rekam medis dari Pima Indians Diabetes Dataset. Dataset terdiri dari 768 sampel dengan delapan fitur kesehatan utama. Proses analisis meliputi pembersihan data, eksplorasi distribusi data, serta persiapan data untuk proses modeling. Jarak antar data dihitung menggunakan rumus Euclidean, dan dilakukan normalisasi agar seluruh fitur memiliki bobot yang seimbang. Data kemudian dibagi menjadi data latih dan uji dengan rasio 80:20. Hasil analisis menunjukkan distribusi kelas yang tidak seimbang, dengan jumlah pasien non-diabetes lebih banyak dibandingkan yang menderita diabetes. Kelompok usia 21–30 tahun mendominasi dalam dataset. Implementasi KNN dalam studi ini menunjukkan bahwa metode ini efektif digunakan untuk klasifikasi medis berbasis data numerik. Penelitian ini mendemonstrasikan potensi KNN sebagai alat bantu diagnosis awal yang praktis dan mudah diimplementasikan dalam sistem kesehatan berbasis data. Kata kunci: K-Nearest Neighbors, prediksi diabetes, machine learning, data medis,                     klasifikasi.
Peran Mahasiswa dalam Program Kampus Mengajar untuk Peningkatan Kualitas Pembelajaran di SDN 177928 Purbasinomba Sianipar, Erika T.; Ruziq, Fahmi
JIPITI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 3 (2025): Agustus 2025 - JIPITI: Jurnal Pengabdian kepada Masyarakat
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Program Kampus Mengajar merupakan bagian dari kebijakan Merdeka Belajar Kampus Merdeka (MBKM) yang bertujuan mendukung peningkatan kualitas pembelajaran di sekolah dasar, khususnya pada aspek literasi, numerasi, serta adaptasi teknologi. Kegiatan ini dilaksanakan di SDN 177928 Purbasinomba, Kabupaten Tapanuli Utara, dengan melibatkan mahasiswa sebagai mitra guru dalam mengajar, membantu administrasi, dan menginisiasi inovasi pembelajaran. Metode pelaksanaan mencakup tahap persiapan, penerjunan, observasi, perencanaan program, implementasi, serta evaluasi hasil. Hasil pengabdian menunjukkan adanya peningkatan minat baca siswa melalui program pojok literasi, optimalisasi perpustakaan, dan pembiasaan membaca sebelum pembelajaran. Pada aspek numerasi, metode pembelajaran kreatif serta pemberian les tambahan berkontribusi terhadap pemahaman konsep matematika. Mahasiswa juga berhasil membantu adaptasi teknologi melalui pembuatan sistem absensi digital dan pengelolaan administrasi berbasis aplikasi. Dampak kegiatan tidak hanya dirasakan oleh siswa dan guru, tetapi juga mahasiswa yang memperoleh pengalaman berharga dalam praktik pedagogik, kepemimpinan, dan kolaborasi. Dapat disimpulkan bahwa program ini efektif meningkatkan kualitas pembelajaran dan dapat direplikasi pada sekolah lain dengan dukungan fasilitas yang memadai.
Implementasi Program Kampus Merdeka melalui Magang Bersertifikat Kebudayaan: Studi di Balai Pelestarian Kebudayaan Wilayah II Sumatera Utara Sianipar, Erika T.; Ruziq, Fahmi
JIPITI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 3 (2025): Agustus 2025 - JIPITI: Jurnal Pengabdian kepada Masyarakat
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Program Magang Bersertifikat Kebudayaan (MBK) merupakan bagian dari Magang dan Studi Independen Bersertifikat (MSIB) yang bertujuan memperkuat kompetensi mahasiswa melalui keterlibatan langsung dalam proses pendataan dan pelestarian kebudayaan. Penelitian ini bertujuan untuk mendeskripsikan hasil implementasi program MBK pada Direktorat Pelindungan Kebudayaan, Direktorat Jenderal Kebudayaan, Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi. Metode yang digunakan meliputi observasi lapangan, wawancara dengan pemangku kepentingan, dokumentasi objek budaya, serta penyusunan produk akhir berupa leaflet, video dokumenter, dan laporan analisis. Hasil menunjukkan bahwa kegiatan magang berkontribusi signifikan terhadap pendataan Objek Pemajuan Kebudayaan (OPK), peningkatan keterampilan riset mahasiswa, serta penyediaan basis data kebudayaan yang terintegrasi melalui sistem DAPOBUD. Pembahasan menyoroti tantangan koordinasi lapangan, keterbatasan akses ke lokasi, dan perlunya strategi penyelarasan informasi antarmentor. Kesimpulan menegaskan bahwa program MBK memiliki nilai strategis dalam mempercepat pelestarian budaya dan meningkatkan kapasitas akademik serta profesional mahasiswa di bidang kebudayaan.
Predicting Smartphone Addiction Levels with K-Nearest Neighbors Using User Behavior Patterns Wayahdi, M. Rhifky; Ruziq, Fahmi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4905

Abstract

Smartphones have become an integral part of everyday life, but their ever-increasing popularity has raised growing global concerns about excessive use (nomophobia), which impacts quality of life, mental health, and academic performance. Existing research often relies on subjective questionnaires, limiting scalability and objectivity. This study addresses this gap by developing a machine learning model to predict smartphone addiction levels through an objective analysis of user behavior patterns. This research evaluates the effectiveness of the K-Nearest Neighbor (KNN) algorithm, identifies the most influential behavioral features, and assesses the model's classification performance. Using a dataset of 3,300 user behavior entries with 11 features, a waterfall-based framework was employed for data preprocessing, model design, and evaluation. The KNN model achieved 95% accuracy in classifying addiction levels. Permutation Feature Importance analysis confirmed ‘App Usage Time’ and ‘Battery Drain’ as the two most influential predictive features. This study demonstrates that KNN is a powerful and viable method for objectively classifying smartphone addiction. The findings provide a strong foundation for developing scalable, AI-driven early detection and intervention systems, offering significant contributions to the fields of computer science and digital well-being.
Web-Based Diabetes Risk Prediction System Using K-NN on Kaggle Early Stage Diabetes Dataset Ruziq, Fahmi; Wayahdi, M. Rhifky
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5277

Abstract

Diabetes mellitus affects approximately 537 million adults globally, and its rising prevalence poses serious health and economic burdens. Early detection is crucial to reduce risks of complications and improve patient outcomes. This study aims to design and implement a web-based diabetes risk prediction system using the K-Nearest Neighbors (K-NN) algorithm to support early detection based on symptoms. The system utilizes the Kaggle Early Stage Diabetes Risk Prediction Dataset containing 520 records with 17 symptom attributes and one class label. Data preprocessing includes converting categorical data into numerical values, discretizing age into predefined ranges, and applying min-max scaling to normalize feature values. K-NN classification was conducted with K values of 1, 3, and 5, using the PHP Machine Learning (PHP-ML) library and MySQL database integration. The system achieved its highest accuracy of 93.46% at K = 1. Manual testing confirmed that the system processes symptom inputs correctly and provides predictions consistent with training data. This web-based tool offers an accessible platform for early diabetes risk screening, supporting self-assessment and triage. It demonstrates that PHP-ML can effectively implement machine learning in a web environment and can be further enhanced through parameter optimization and integration with larger, more diverse datasets to strengthen generalization.
Decision Support System for Determining the Best Employee with Web-Based AHP Method at Pariwisata Polytechnic Syahputra, Ilham; Ruziq, Fahmi; Rambe, Aripin
Journal of Technology and Computer Vol. 1 No. 2 (2024): May 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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A Decision Support System (DSS) for Best Employee Determination Selection using the Analytical Hierarchy Process (AHP) method is an essential tool for enhancing employee performance and motivation. The awards given by companies to their best employees serve as a powerful incentive, driving each employee to consistently deliver their best work. To determine the best employee, companies assess performance over a specific period, considering various criteria. At Medan Tourism Polytechnic, these criteria include work behavior, work discipline, honesty, loyalty, and cooperation. The AHP method structures the decision-making process into a hierarchical model, allowing for systematic evaluation of alternative choices against the set criteria. By assigning weights to each criterion, AHP quantifies subjective assessments, providing an objective basis for comparing employees. This method ensures transparency and fairness in the selection process, promoting a culture of excellence and motivation among employees. Implementing a DSS with the AHP method not only simplifies the evaluation process but also ensures that decisions are based on measurable performance indicators. This fosters an environment where employees are encouraged to continuously improve their performance, contributing to the overall productivity and success of the organization. This system is integral in maintaining high standards and employee satisfaction within the organization.
Implementation of Data Mining to Predict the Eligibility Level for Prospective KPR (Home Ownership Credit) Subsidized Housing Customers Mitra Griya Indah Using the C4.5 Algorithm Anggraini, Mia; Ruziq, Fahmi; Nuary Singarimbun, Roy
Journal of Technology and Computer Vol. 1 No. 2 (2024): May 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

In the housing industry, data mining plays an important role in assisting the home loan application process by extracting knowledge from historical data, this process allows lenders to identify potentially high-risk home loan applicants and decide whether to approve or reject the loan application. Data mining helps in effective marketing strategies. By optimizing this process, response time to home loan applications can be accelerated, operational efficiency increased, and credit risk can be better managed. In the practice of providing KPR (Home Ownership Credit) to prospective consumers, there are possible problems that will occur like most other people, namely late installment payments or defaulted payments so that it will make it difficult for the bank to maintain the level of credit risk on the credit provided, this is because Mitra Griya Indah Housing has not paid much attention to data regarding the history of credit granting decisions, in other words, it has not maximally utilized data on previous credit granting decisions in supporting credit granting decisions. To solve this problem, the researcher designed a calculation information system. In this case the author uses the waterfall method in the research process. For system design, the author uses the PHP programming language with a database format using MySql. Finally, with this information system, it can facilitate the decision-making process for prospective customers of Home Ownership Credit.
Implementation of Data Mining on Sales Data of Bambu Ungu Cafe to Find out Consumer Purchasing Patterns Using the Apriori Algorithm Fadli, Rahmad; Ruziq, Fahmi; Imam, Chairul
Journal of Technology and Computer Vol. 1 No. 2 (2024): May 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

In today's digital age, restaurants or cafes face increasing competition and complex challenges. To stay relevant and succeed in this business, the use of data mining is crucial, as it becomes a valuable tool in optimizing sales, increasing customer satisfaction, and achieving long-term success in the restaurant and café industry. Data mining helps in effective marketing strategies. By analyzing customer data, purchase information, and preferences restaurants and cafes can identify different customer segments and create customized marketing. With so much sales transaction data, it will certainly be difficult if the data is analyzed manually, therefore information will be obtained if there is processing with the help of a system to get sales patterns. The results of this processing will produce transaction information to support product transaction decisions. To solve this problem, the researcher designed a calculation information system. In this case the author uses the waterfall method in the research process.  For system design the author uses the PHP programming language with a database format using MySQL. Finally, with this information system, the calculation process can be done automatically without the need to calculate manually is appropriate, provided that all data inputted is valid.
Implementation of the Course Scheduling Model at State Vocational High School 4 Medan Swanda, Ari; Syahputra, Dinur; Ruziq, Fahmi
Journal of Technology and Computer Vol. 1 No. 3 (2024): August 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Course scheduling is one of the most common problems in education. The number of rooms, the number of teachers, the number of subjects that must be taught, and the teaching hours of teachers who teach not only one school so that a method is needed so that there is no block with other schedules. In designing a course schedule with a limited number of teachers and there are teachers who cannot teach on certain days will be a complicated problem to be solved manually. adaptive which is commonly used to solve a value search in an optimization problem. Adaptive is commonly used to solve a value search in an optimization problem. i.e. the progression of generations in a natural population slowly follows the principle of natural selection or "he who is strong, survives". By emulating this theory, it can be used to find solutions to real-world problems. By applying the course schedule design time and teachers can choose the time to teach according to their time choice so that the course schedule design can be completed faster than the course schedule design. Can be completed faster than the manual schedule design.
Design of a Web-based Correspondence System Using the Codeigniter 3 Framework (Case study: Battuta University) Intan Pratiwi, Dea Balqis; Harahap, Baginda; Ruziq, Fahmi
Journal of Technology and Computer Vol. 1 No. 4 (2024): November 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

In the digital age, technology plays an important role in educational institutions such as Battuta University, helping in streamlining administrative processes and data management. The current reliance on Microsoft Excel for document management at the university proved to be inefficient, leading to issues such as misplaced documents, tracking difficulties, and difficulties in archivist management. To address these challenges, the development of a web-based document management system using the Codeigniter Framework is proposed. By utilizing this system, users can easily access and manage documents from various devices. So that the implementation of such a system is expected to improve the process of handling, tracking, and archiving documents, thus benefiting the university, especially in the field of correspondence.
Co-Authors Anggraini, Mia Aripin Rambe Baginda Harahap Chairul Imam Dewi Wahyuni Dewi Wahyuni Dinur Syahputra Ellanda Purwawijaya Fachry Abda El Rahman Fithrie Soufitri Ginting, Subhan Hafiz Nanda Hardianti, Eva Hutasoit, Rutmian Mariama Ilham Syahputra Intan Pratiwi, Dea Balqis Irna Herliza Rizki Junaidi Lubis M. Rhifky Wahyadi M. Rhifky Wayahdi Mayang Mughnyanti Melky Suhery Simamora Muhammad Furqon Siregar Nanda Ginting, Subhan Hafiz Nasution, Auliana Nuary Singarimbun, Roy Nurhajijah, Nurhajijah Rahmad Fadli Rahmad Syuhada Rambe, Aripin Sattar Rasul Sianipar, Erika T. Siti Farida Sridewi, Nurmala Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Surya Darma Swanda, Ari Syafitri Ramadhani Syahputra, Dinur Weima, Zhang