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All Journal Jurnal Riset Informatika Aptekmas : Jurnal Pengabdian Kepada Masyarakat
Nita Novita
Politeknik Negeri Sriwijaya
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WEBSITE SEBAGAI SARANA PROMOSI UKM SOPYAN Indri Ariyanti; Nita Novita; Delta Khairunnisa; Aris Ganiardi
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 3 No 3 (2020): APTEKMAS Volume 3 Nomor 3 September 2020
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.376 KB) | DOI: 10.36257/apts.v3i3.2125

Abstract

Sopyan UKM is one of Palembang's craftsmen of Palembang woven fabric cloths / tajung located in the Tuan Kentang area of Palembang which is led by Mr. Sopyan Candra. The internet is a tool used by UKM Sopyan in introducing and promoting its products. Both through the website and social media such as Instagram, line, and WhatsApp. For this reason, counseling needs to be given on how to create a Website and introduce equipment used in the process of making woven woven cloths / tajung to Sopyan Palembang UKM.
PEMANFAATAN INTERNET LEARNING SEBAGAI MEDIA PEMBELAJARAN MULTI PLATFORM PADA MASA PANDEMI COVID-19DEMI COVID-19 Muhammad Aris Ganiardi; Nita Novita; Indri Ariyanti; Delta Khairunnisa
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 5 No 3 (2022): APTEKMAS Volume 5 Nomor 3 2022
Publisher : Politeknik Negeri Sriwijaya

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Abstract

Misi dan visi utama Sekolah Islam Terpadu Prima Insani Palembang adalah menyelenggarakan pendidikan formal yang islami. Untuk mewujudkan misi dan visi tersebut maka Sekolah Islam Terpadu Prima Insani Palembang melaksanakan tiga bentuk pendidikan yaitu pendidikan formal di pagi dan siang hari dalam bentuk taman kanak-kanak, sekolah dasar, dan sekolah menengah pertama. Pada masa pandemi Covid-19 ini penyelenggaraan proses pembelajaran di Sekolah Islam Terpadu Prima Insani tidak berjalan dengan baik. Proses pembelajaran banyak dilakukan dalam bentuk daring dengan memanfaatkan teknologi internet. Sayang proses pembelajaran ini belum berjalan dengan baik karena keterbatasan pengetahuan dan keterampilan para guru memanfaatkan teknologi internet sebagai media pembelajaran digital yang terstruktur dengan baik sebagai pendukung proses pembelajaran pada masa pandemi Covid-19. Kegiatan pengabdian kepada masyarakat kerjasama dosen-mahasiswa ini akan memberikan pelatihan dan penyuluhan agar dapat membantu para guru untuk memanfaatkan teknologi internet learning sebagai media pembelajaran digital.
A COMPARATIVE STUDY OF DISTANCE METRICS AND NEIGHBOR SELECTION IN K-NEAREST NEIGHBOR FOR VOCATIONAL STUDENT PERFORMANCE CLASSIFICATION Muhammad Aris Ganiardi; Ida Wahyuningrum; Nita Novita; Denny Alfian
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.520

Abstract

This study aims to evaluate parameter sensitivity in the K-Nearest Neighbor (KNN) algorithm, particularly the selection of distance metrics and k-values, for classifying academic performance in vocational education with heterogeneous and imbalanced data characteristics. The dataset consists of 750 first-year students from the Informatics Management program, including academic attributes (GPA, attendance, and core course grades) and demographic attributes (age, gender, educational background, and economic status). Data preprocessing involves data cleaning, one-hot encoding, Z-score normalization, and handling class imbalance using SMOTE. Model evaluation is conducted using K-Fold Cross Validation with accuracy, precision, recall, and macro-average F1-score as performance metrics. The results show that KNN performance is highly influenced by the combination of distance metrics and k-values. All metrics achieve accuracy above 84%, but differ in handling class imbalance. The Chebyshev metric (k = 10) provides the best balance with an F1-score of 0.6468, while the Minkowski metric (p = 3) achieves the highest recall of 0.7334. The Euclidean metric attains the highest accuracy of 0.8504 (k = 11), but tends to be biased toward the majority class. These findings indicate that optimizing KNN parameters should not rely solely on accuracy, but also consider balanced performance across classes. This study provides a practical evaluation framework for selecting KNN parameters to support more robust and fair academic prediction systems in vocational education data.
Co-Authors Aris Ganiardi delta khairunnisa Denny Alfian Ida Wahyuningrum Indri Ariyanti Muhammad Aris Ganiardi