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All Journal Jurnal Ilmiah Universitas Batanghari Jambi JSiI (Jurnal Sistem Informasi) Jurnal Tekno Insentif Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Sisfotek Global Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Sisfotek Global Academic Journal of Computer Science Research (AJCSR) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Tren Bisnis Global JPEG Jurnal Topik Global
M. Bucci Ryando
Institut Teknologi dan Bisnis Bina Sarana Global
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Perbandingan Algoritma K-Nearest Neighbor dan Naive Bayes untuk Klasifikasi FoMO Pengguna Media Sosial Haromaen, Muhammad; Piskana, Marthin; Ryando, M. Bucci; Hadinata, Wira
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2784

Abstract

The intensive use of social media among students poses a risk of triggering Fear of Missing Out (FoMO), which negatively affects mental health and learning focus. This study aims to develop a classification model to detect FoMO tendencies among students at SMAN 11 Kabupaten Tangerang. A quantitative approach was used, employing the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms. The analyzed variables include gender, duration of social media use, access frequency, desire to stay updated, and its impact on productivity. Data were collected from 244 respondents and processed through pre-processing, modeling, and evaluation stages. Validation results show that KNN achieved the highest accuracy at 94.69%, while Naïve Bayes reached 93.06%. These findings indicate that KNN is more effective in detecting FoMO tendencies based on numerical data and has the potential to support early intervention efforts in educational settings.Keywords: Fear of Missing Out; K-Nearest Neighbor; Social Media; Classification; Naive Bayes AbstrakPenggunaan media sosial secara intensif di kalangan pelajar berisiko memunculkan gejala Fear ofaMissing Out (FoMO), yang berdampak negatif pada kesehatan mental dan fokus belajar. Penelitian ini bertujuan untuk mengembangkan model klasifikasi kecenderungan FoMO pada pelajar SMAN 11 Kabupaten Tangerang. Metode yang digunakan adalah pendekatan kuantitatif dengan algoritma K-NearestiNeighbor (KNN) dan NaïveiBayes. Variabel yang dianalisis meliputi jenis kelamin, durasi penggunaan media sosial, frekuensi akses, keinginan untuk tetap update, dan pengaruh terhadap produktivitas. Data dikumpulkan dari 244 responden dan diproses melalui pre-processing, modeling, dan evaluasi. Hasil validasi menunjukkan bahwa KNN menghasilkan akurasi tertinggi sebesar 94,69%, sementara Naïve Bayes mencapai 93,06%. Temuan ini menunjukkan bahwa KNN lebih efektif untuk mendeteksi kecenderungan FoMO berbasis data numerik dan berpotensi mendukung pengembangan intervensi dini dalam konteks pendidikan.Kata kunci: Fear of Missing Out; K-Nearest Neighbor; Media Sosial; Klasifikasi; Naive Bayes
Sistem Pendukung Keputusan Kontrak Kerja Karyawan Probation Menggunakan Metode Fuzzy Simple Additive Weighting Ash shidiq, Ammar Yusuf; Siddiq, Hafizh; Utomo, Prawido; Ryando, Muhammad Bucci
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2907

Abstract

The evaluation of probationary employees is crucial in determining their eligibility for contract extension. The assessment typically involves aspects such as performance, discipline, skills, interpersonal abilities, integrity, and appearance, but often this assessment remains subjective. This study integrates fuzzy logic to reduce uncertainty and applies the Simple Additive Weighting (SAW) method to assign weights objectively. The developed system enables a more fair and measurable evaluation. Results indicate that employees 1, 2, and 4 are eligible for contract extension. Performance testing yielded an accuracy of 75%, with both precision and recall at 66.7%, demonstrating alignment between the system's recommendations and the HR department's decisions. This method serves as a decision support tool that promotes a more objective and structured decision-making process.Keywords: Employee Evaluation; Decision Support System; Fuzzy-SAW Method AbstrakEvaluasi terhadap karyawan probation penting dilakukan dalam penentuan kelayakan perpanjangan kontrak kerja. Penilaian mencakup aspek kinerja, kedisiplinan, keterampilan intrapersonal, integritas, dan penampilan, namun penilaian ini kerap bersifat subjektif. Penelitian ini menggabungkan logika fuzzy untuk mengurangi ketidakpastian dan metode Simple Additive Weighting (SAW) guna menentukan bobot secara objektif. Sistem yang dikembangkan menghasilkan penilaian lebih adil dan terukur. Hasil menunjukkan bahwa karyawan 1, 2, dan 4 layak diperpanjang kontraknya. Pengujian performa menghasilkan akurasi 75%, dengan precision dan recall sebesar 66,7%, yang menunjukkan kesesuaian sistem dengan keputusan HRD. Metode ini berfungsi sebagai alat pendukung yang membantu proses pengambilan keputusan agar lebih objektif dan terukur.Kata kunci: Penilaian Kinerja Karyawan; Pengambilan Keputusan; Metode Fuzzy-SAW
Co-Authors Achmad Sidik Akbar, Muhammad Ikmal Arni Retno Mariana Asep Rio Saputra Ash shidiq, Ammar Yusuf Batu Bara, Muhamad Aldi Mudin Bayu Irfan Aditya Bijaksana, Rhafael Dedi Dedi Dedi Dedi Eka Sumiati Ferawati Ferawati Ferry Maulana Ibrahim Fredy Susanto Hadinata, Wira Haerunnisa, Siti Halim Agung Haromaen, Muhammad Hidayatullah, Ilham Iqbal, Muchamad Khoirul Anwar Makmur Raya Maruf, Rohim Muhamad Naufal Robih Nabila Ana Mantika Nova Teguh Sunggono Novara Aulist Zakia Nur Asiyah Nurholipah Nurholipah Nurmaesah, Nunung Piskana, Marthin Pudoli, Ahmad Rahmat Tullah Ramadhanti, Nur Fairus Raya, Makmur Ridwan Abdul Hakim Saepul Nazar Septihadi, Ali Nugroho Setiawan, Prayoga Siddiq, Hafizh Sidik , Achmad Sidik, Achmad Siti Maesaroh Sri Rahayu Sutarman Sutarman Syahidah, Kurnia Syifa Aulia Tasya Amelia Tina Amalia Triono Triono Utomo, Prawido Wahyu Nurifai Widiawati Widiawati Zainul Hakim