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All Journal Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unram Journal of Community Service (UJCS) Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Computer Science and Information Technology Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Computer and Technology
Akbar, Ardiyallah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Jurnal Computer and Technology

 1 
SISTEM PAKAR DIAGNOSIS PENYAKIT PADA TANAMAN KACANG HIJAU BERBASIS WEB MENGGUNAKAN METODE DEMPSTER SHAFER Hamim, Lutfi; Imran, Bahtiar; Akbar, Ardiyallah
Journal Computer and Technology Vol. 1 No. 1 (2023): Juli 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v1i1.77

Abstract

Kacang hijau merupakan salah satu tanaman kacang-kacangan yang penting di Indonesia karena memiliki banyak gizi dan sebagai sumber pangan berprotein nabati tinggi. Namun dalam perkembangannya banyak pula tanaman kacang hijau yang terserang berbagai macam penyakit apalagi pada saat musim penghujan. Ketersediaan Waktu penyuluhan pun minim disebabkan banyaknya lokasi yang harus dikunjungi.Oleh karena itu perlu adanya sistem pakar yang mampu memberikan informasi tentang penyakit penyakit tanaman kacang hijau dan solusi permasalahannya. Penelitian ini bertujuan untuk membangun sistem pakar yang dapat mendiagnosa 3 jenis penyakit pada tanaman Kacang Hijau dari 17 gejala dengan menggunakan metode Dempster-Shafer berbasis website. Hasil uji coba menunjukkan bahwa sistem pakar ini memiliki tingkat akurasi diagnosa sebesar 80%, yang menunjukkan efektivitasnya dalam membantu mengidentifikasi jenis penyakit yang menyerang tanaman kacang hijau. Metode Dempster-Shafer yang digunakan dalam sistem ini memungkinkan penggabungan informasi dari berbagai gejala penyakit, sehingga memberikan hasil diagnosa yang lebih tepat dan akurat.
CYBER BULLYING SENTIMENT ANALYSIS BASED ON SOCIAL CATEGORIES USING THE CHI-SQUARE TEST Hadi, Zulpan; Suryadi, Emi; Akbar, Ardiyallah; Zaenudin; Muslim, Rudi
Journal Computer and Technology Vol. 2 No. 1 (2024): Juli 2024
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v2i1.144

Abstract

This research evaluates various machine learning models in classifying sentiment in cyberbullying data across six categories: not_cyberbullying, gender, religion, other_cyberbullying, age, and ethnicity. Using a Bag of Words approach combined with Chi-Square feature selection (1000 features), models tested include SVM, Logistic Regression, Naïve Bayes, KNN, and Random Forest. Results show SVM and Logistic Regression achieving the highest accuracy at 83%, indicating their effectiveness in prediction. Naïve Bayes performed the poorest with 62% accuracy, suggesting a mismatch with the data or need for further tuning. KNN and Random Forest showed good performance with 75% and 81% accuracy respectively, though not as high as SVM and Logistic Regression. This multi-algorithm approach provides insights into each model's effectiveness and behavior on diverse data characteristics, essential for understanding the unique nuances of each cyberbullying category. Model selection should consider accuracy, interpretability, computational cost, and suitability to specific problem characteristics. This research aims to deepen understanding of cyberbullying to support more effective mitigation strategies.
Co-Authors ahmad yani Amiruddin Kalbuadi Buchtami, Lalu Reza Hambali Hambali Hamim, Lutfi Hamzan Wadi Imran, Bahtiar Jaelani, Lalu Rizki Juniyardi, Lalu Karim, Muh Nasirudin Kembang, Lale Puspita Lalu Darmawan Bakti, Lalu Darmawan Lalu Delsi Samsumar, M.Eng. Rudi Muslim Sriasih, Sriasih Subki, Ahmad Supardianto Supardianto Suryadi, Emi Tony Gunawan Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zulpan Hadi