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All Journal Jurnal Teknologi Informasi MURA Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi Mura
Tiska Pattiasina
Politeknik Negeri Ambon
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Public Health Other

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PERBANDINGAN KINERJA ALGORITMA SVM DAN NAIVE BAYES PADA KLASIFIKASI PRESTASI AKADEMIK SISWA: STUDI KASUS SMAS BPD TOBELO SELATAN Tiska Pattiasina; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete; Jupriyanto Jupriyanto
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2915

Abstract

Students’ academic achievement is an important indicator of the success of the educational process; however, its assessment is often subjective and not yet fully data-driven. Therefore, a systematic analytical approach is required to classify students’ academic achievement objectively and accurately. This study aims to compare the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying the academic achievement of grade III students at SMAS BPD Tobelo Selatan. A data mining approach using classification techniques was applied, involving 17 attributes as predictor variables and two target classes of academic achievement, namely Very Good and Good. Data processing and model evaluation were conducted using the WEKA software, with performance measured through accuracy, precision, recall, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results indicate that the SVM algorithm achieves the best performance in terms of accuracy, precision, and recall, each reaching 97.78%, while the Naive Bayes algorithm obtains the highest AUC-ROC value of 98.08%. These findings demonstrate that SVM is superior in prediction accuracy, whereas Naive Bayes shows excellent capability in class discrimination. This study is expected to support data-driven academic decision-making in school environments.
Klasifikasi Persiapan Keuangan Mahasiswa Tingkat Akhir dalam Menghadapi Dunia Kerja Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor Tiska Pattiasina; Stenly Ronaldo Titioka; Frangky Jansens Louth; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3154

Abstract

Financial preparation is an important factor for final year students in facing the world of work. This study aims to classify the financial readiness of final year students using the Naive Bayes and K-Nearest Neighbor (K-NN) algorithms based on 15 attributes related to economic conditions, financial behavior, and financial literacy. Data were obtained from 85 final year students of the Business Administration Department of Ambon State Polytechnic and processed using the Synthetic Minority Over-sampling Technique (SMOTE) technique to address class imbalance. Testing was conducted using WEKA software with a 10-fold cross-validation method. The results showed that the Naive Bayes algorithm produced an accuracy of 96.6667%, a precision of 96.7%, a recall of 96.7%, and an ROC Area of ​​0.9988. Meanwhile, the K-Nearest Neighbor (K-NN) algorithm produced an accuracy of 80.0%, a precision of 80.4%, a recall of 80.0%, and an ROC Area of ​​0.8703. These results indicate that Naive Bayes outperforms K-NN in classifying the financial readiness of final-year students. Furthermore, the application of SMOTE has been shown to improve the model's ability to recognize minority classes, resulting in a more balanced and representative classification.
Klasifikasi Persiapan Keuangan Mahasiswa Tingkat Akhir dalam Menghadapi Dunia Kerja Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor Tiska Pattiasina; Stenly Ronaldo Titioka; Frangky Jansens Louth; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3154

Abstract

Financial preparation is an important factor for final year students in facing the world of work. This study aims to classify the financial readiness of final year students using the Naive Bayes and K-Nearest Neighbor (K-NN) algorithms based on 15 attributes related to economic conditions, financial behavior, and financial literacy. Data were obtained from 85 final year students of the Business Administration Department of Ambon State Polytechnic and processed using the Synthetic Minority Over-sampling Technique (SMOTE) technique to address class imbalance. Testing was conducted using WEKA software with a 10-fold cross-validation method. The results showed that the Naive Bayes algorithm produced an accuracy of 96.6667%, a precision of 96.7%, a recall of 96.7%, and an ROC Area of ​​0.9988. Meanwhile, the K-Nearest Neighbor (K-NN) algorithm produced an accuracy of 80.0%, a precision of 80.4%, a recall of 80.0%, and an ROC Area of ​​0.8703. These results indicate that Naive Bayes outperforms K-NN in classifying the financial readiness of final-year students. Furthermore, the application of SMOTE has been shown to improve the model's ability to recognize minority classes, resulting in a more balanced and representative classification.
PELATIHAN MANAJEMEN BISNIS DIGITAL BAGI UMKM DI JEMAAT IMANUEL OSM KOTA AMBON Tiska Pattiasina; Frangky Jansens Louth; Marie Chrestien Tahalele; Jansen Roland Patty
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 7 No. 3 (2026): Vol. 7 No. 3 (2026)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v7i3.59559

Abstract

Perkembangan teknologi digital telah membawa perubahan signifikan dalam dunia usaha, khususnya bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM). Pemanfaatan teknologi digital tidak hanya mendukung kegiatan pemasaran, tetapi juga membantu pengelolaan keuangan dan pengembangan identitas usaha secara lebih profesional. Namun demikian, masih banyak pelaku UMKM yang belum memiliki keterampilan yang memadai dalam memanfaatkan teknologi digital untuk mendukung pengembangan usaha mereka. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan keterampilan pelaku UMKM di Jemaat Imanuel OSM Kota Ambon dalam menerapkan manajemen bisnis digital. Materi yang diberikan meliputi pemasaran digital menggunakan media sosial, penyusunan laporan keuangan sederhana menggunakan Microsoft Excel, dan pemanfaatan ChatGPT untuk pembuatan desain logo usaha. Kegiatan dilaksanakan pada tanggal 18 April 2026 secara luring dengan melibatkan 17 peserta yang merupakan anggota Jemaat Imanuel OSM Kota Ambon. Metode yang digunakan meliputi ceramah, diskusi, demonstrasi, praktik langsung, dan tanya jawab. Evaluasi dilakukan menggunakan kuesioner Skala Guttman untuk mengetahui respon peserta terhadap pelaksanaan kegiatan. Hasil evaluasi menunjukkan bahwa seluruh peserta memberikan respon positif terhadap kegiatan yang dilaksanakan. Kegiatan ini berhasil meningkatkan pemahaman peserta mengenai pentingnya digitalisasi usaha dalam aspek pemasaran, pengelolaan keuangan, dan pengembangan identitas bisnis.
Co-Authors Andrie CH Salhuteru Andrie CH Salhuteru Febiola Matuankotta Febiola Matuankotta Frangky Jansens Louth Grace Fredriksz Grace Fredriksz Jansen Roland Patty Join Rachel Luturmas Join Rachel Luturmas Jupriyanto . Laura S Nunumete Laura S Nunumete Marie Chrestien Tahalele Stenly Ronaldo Titioka