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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Advances in Data and Information Systems INFOKUM Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Minfo Polgan (JMP) Journal of Science Technology (JoSTec) Jurnal Pengabdian Masyarakat Nusantara Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Pengabdian kepada Masyarakat Jurnal Pustaka Dianmas Journal of Technology and Computer (JOTECHCOM) LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics JIPITI: Jurnal Pengabdian kepada Masyarakat International Journal of Applied Mathematics and Computing. International Journal of Educational Insights and Innovations (IJEDINS) Outline Journal of Community Development Komunitas: Hasil Kegiatan Pengabdian Masyarakat Indonesia
M. Rhifky Wayahdi
Universitas Battuta
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Veterinary Other

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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.
Penerapan Ilmu Sistem Informasi untuk Efisiensi Manajemen Kearsipan di Universitas Battuta Davita, Davita; Wayahdi, M. Rhifky
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

Kegiatan pengabdian kepada masyarakat ini berfokus pada penerapan praktis ilmu sistem informasi dalam lingkungan kerja nyata, khususnya pada sistem persuratan dan kearsipan di Universitas Battuta. Pentingnya kegiatan ini adalah untuk menjembatani kesenjangan antara teori akademis dengan praktik profesional yang dihadapi mahasiswa. Metode yang digunakan dalam pengabdian ini adalah partisipasi aktif yang meliputi observasi alur kerja, wawancara dengan staf, pencatatan data kinerja sistem, serta dokumentasi proses bisnis di bagian persuratan. Hasil dari kegiatan ini menunjukkan bahwa mahasiswa mampu mengaplikasikan konsep-konsep dari mata kuliah seperti Manajemen Data dan Informasi, Analisis Sistem, dan Etika Profesi secara langsung untuk membantu operasional institusi. Selain itu, kegiatan ini berhasil meningkatkan keterampilan teknis dan soft skills yang esensial bagi mahasiswa sebagai persiapan memasuki dunia kerja. Kesimpulannya, program ini terbukti efektif sebagai sarana hilirisasi ilmu pengetahuan yang memberikan nilai tambah bagi institusi mitra serta meningkatkan kompetensi mahasiswa secara signifikan.
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 Students Final Project Title Acceptance at Ganesha Polytechnic Medan using Analytical Hierarchy Process (AHP) Method Ramadhan, Wisnu; Wayahdi, M. Rhifky; Hasibuan, Eka Hayana
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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Abstract

Many students are confused about determining the title that has been standardized by the head of study program. Is the title they are proposing relatively easy and in accordance with what is standardized by the head of study program or could it actually make things difficult for the student? The decision making system method used is Analytical Hierarchy Process (AHP) with the criteria of level of difficulty, reference source, number of similar titles and reference accreditation. The manufacturing stages carried out in this research used the waterfall and web-based method. Making this application uses data processing procedures, Data Flow Diagrams and MySQL DBMS. The output of the research I have made is that it can make it easier for students to submit the title of their final assignment, making it easier for the head of study program to sort out whether the title that will be submitted by the student is in accordance with the standards set by the head of study program and can also assess at the same time whether the title is easy and suitable for use as a final assignment.
Decision Making System for Educator Recruitment at IP Daarul Arqam Private Junior High School using Simple Additive Weighting Method Anzani, Nurul; Wayahdi, M. Rhifky; Purwawijaya, Ellanda
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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Abstract

An institution can work well to achieve its goals which are determined by many factors. Teachers play an important role in improving the life of a nation. Teachers have great obligations and responsibilities in terms of educating the nation's children. Therefore, a school has several criteria in selecting educators. For this reason, accuracy and examination are needed in selecting educators to get qualified teachers. By using a Web-based decision support system the problems faced by foundations or schools can be overcome, so that subjectivity in decision making can be reduced. This system can integrate data on prospective educators from various sources, such as education, work experience, expertise, and others. In addition, this system can also apply predetermined decision-making methods, such as the Simple Additive Weighting (SAW) method. By using this system, decision makers can easily access and analyze prospective educators' data comprehensively. They can also see the results of ranking prospective educators based on relevant criteria. This allows decision makers to make more objective and informed decisions in determining teaching staff. With this application, it can make it easier to make decisions on determining teaching staff.
Design of a Website-Based Battuta University Employee Payroll System Nur Alisya, Siti; Harahap, Baginda; Wayahdi, M. Rhifky
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

Everything in this world is bound to change, including technological advancements. Technology will never stop developing. The exchange of data and information is now very fast, even in less than a second. At Battuta University, in processing employee salaries, they still use manual calculations and use the Ms. Excel application. This can cause errors in calculating employee salaries and the process of printing pay slips and employee payroll reports that take a long time. In solving this problem, the author designs a website-based employee payroll application. In designing the application, the author uses a system development methodology, namely the waterfall and qualitative methods, with the application program language made in PHP and MySQL database. The results of this design produce a computerized application program which will be used to process web-based employee salaries at Battuta University and it is hoped that the university will find it easier to input data, compile payroll reports faster and more efficiently.
Decision Support System to Determine the Best Student at MAS Islamic Center in Class XI using a Simple Additive Weighting Method Thania, Sheila Try; Wayahdi, M. Rhifky; Mughnyanti, Mayang
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

This research aims to develop a Decision Support System (DSS) in determining the best students at MAS ISLAMIC CENTRE by using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of its ability to calculate the number of performance weights on each alternative effectively, allowing comparison of various options based on specified criteria. This research uses a quantitative approach with system development that follows the Waterfall model, starting from the needs analysis stage to implementation and maintenance. The results show that the designed system is able to process student data accurately and display student rankings quickly and efficiently. This provides an advantage for schools in making decisions that are more objective and supported by solid data. The implementation of SAW-based DSS is expected to be a solution that supports transparency and effectiveness in determining the best students in the educational environment.
Sistem Pendukung Keputusan Seleksi Karyawan Baru dengan Simple Additive Weighting pada PT. Technology Laboratories Indonesia Fahmi Ruziq; M. Rhifky Wayahdi
Jurnal Minfo Polgan Vol. 11 No. 2 (2022): Article Research
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v11i2.13506

Abstract

Perkembangan teknologi informasi yang pesat memberikan dampak signifikan dalam berbagai aspek kehidupan, memudahkan pemrosesan data, analisis data, dan menghasilkan informasi relevan. Sistem Pendukung Keputusan (SPK) dengan metode Simple Additive Weighting (SAW) menjadi langkah strategis dalam pengambilan keputusan. Studi ini mengevaluasi aplikasi SAW pada seleksi karyawan baru PT. Technology Laboratories Indonesia dan mengintegrasikannya dengan metode pengumpulan data melalui studi lapangan dan studi pustaka. Hasil normalisasi dan perangkingan memberikan peringkat yang efisien dalam menentukan karyawan terpilih. Kesimpulannya, penerapan SAW pada SPK dapat menyederhanakan proses seleksi, meningkatkan efisiensi, transparansi, dan obyektivitas, memberikan dampak positif bagi pengelolaan organisasi, serta mendukung pengambilan keputusan yang lebih terinformasi.
Pengenalan Dasar Pemrograman dengan Scratch untuk Anak Sekolah Dasar di Sanggar Keadilan SMH-Indonesia Wayahdi, M. Rhifky; Ruziq, Fahmi
JIPITI: Jurnal Pengabdian kepada Masyarakat Vol. 1 No. 2 (2024): Agustus 2024 - JIPITI: Jurnal Pengabdian kepada Masyarakat
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Kegiatan pengabdian kepada masyarakat "Pengenalan Dasar Pemrograman dengan Scratch untuk Anak Sekolah Dasar di Sanggar Keadilan SMH-Indonesia" bertujuan untuk meningkatkan pengetahuan dan keterampilan dasar pemrograman pada anak-anak. Kegiatan ini dilaksanakan dengan metode pembelajaran interaktif, menarik, dan menyenangkan, serta menggunakan media pembelajaran yang menarik dan mudah dipahami. Hasilnya, terjadi peningkatan yang signifikan pada semua indikator penilaian setelah kegiatan dilaksanakan. Hal ini menunjukkan bahwa kegiatan ini telah mencapai tujuannya dalam meningkatkan pengetahuan dan keterampilan dasar pemrograman, serta meningkatkan pemikiran komputasi, minat, dan motivasi anak-anak dalam belajar pemrograman. Kegiatan ini diharapkan dapat membantu anak-anak untuk mempersiapkan diri untuk masa depan di era digital.
Co-Authors Anzani, Nurul Baginda Harahap Davita, Davita Dewi Wahyuni Dewi Wahyuni Dinur Syahputra Dinur Syahputra Dinur Syahputra Ellanda Purwawijaya Ginting, Subhan Hafiz Nanda Guntur, Surya Hasibuan, Eka Hayana Hutasoit, Rutmian Mariama Ihsan Primas Danu John John Manza, Yuke Mughnyanti, Mayang Nanda Ginting, Subhan Hafiz Nur Alisya, Siti Nurhajijah, Nurhajijah Ramadhan, Wisnu Ramadhani, Syafitri Rutmian Mariama Rutmian Mariama Hutasoit ruziq, fahmi Sheila Try Thania Sridewi, Nurmala Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Subhan Hafiz Nanda Ginting Surya Darma Surya Darma Surya Guntur Syed Hassan Ali Thania, Sheila Try Zaki, Mohammed