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All Journal Jurnal Simetris Jurnal Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) Jurnal Teknologi dan Informasi PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Agus Nursikuwagus
University of Computer Indonesian (UNIKOM)
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Metode Classifier Gaussian Naïve Bayes Untuk Klasifikasi Konsumsi Makanan Cepat Saji Pada Tingkat Resiko Obesitas Nursikuwagus, Agus; Suherman, Suherman
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10650

Abstract

Proses klasifikasi pada konsumsi makanan cepat saji berdasarkan fakta nutrisi di menu McDonald’s tentunya perlu dilakukan karena untuk mengetahui pola konsumsi makanan dengan kandungan yang beresiko menyebabkan obesitas Obesitas merupakan salah satu penyakit yang tidak menular, akan tetapi banyak terjadi di kalangan remaja. Salah satu dampak dari adanya penyakit obesitas ini yaitu pesatnya arus globalisasi yang memberikan kemudahan pada pengaruh pola hidup, salah satunya yaitu pola konsumsi makanan. Dengan mengkonsumsi makanan cepat saji yang berlebihan maka akan meningkatkan seseorang itu terkena penyakit obesitas. Penelitian ini bertujuan untuk mengetahui pengklasifikasian serta pengelompokan mengenai makanan cepat saji yang nantinya dapat dilakukan prediksi apakah seseorang itu beresiko terkena penyakit obesitas ataupun tidak beresiko terkena penyakit obesitas. Algoritma yang digunakan pada penelitian ini yaitu model Gaussian Naïve Bayes menggunakan Bahasa pemrograman python yang kemudian akan dilakukan pembagian data training dan data testing untuk dilihat seberapa besar nilai akurasinya. Data fakta nutrisi pada makanan cepat saji di McDonald’s ini terdapat 500 dataset menggunakan parameter menu calories, cholesterol, sodium, carbohydrates, sugars, protein, vitamin, calcium, fat, iron, fiber, potassium, minerals, dan condition. Dengan implementasi yang telah dilakukan klasifikasi yaitu 20% data testing dengan jumlah data sebanyak 100 data. Akurasi dicapai 80% dari data training dengan jumlah data sebanyak 400 data
Hyper Parameter Tuning of Multilayer Convolutional Network and Augmentation Method for Classification Motive of Batik Nursikuwagus, Agus; hartono, tono; Nurwicaksono, M A; Choir, M M; Saputri, M A
Jurnal Informatika Vol. 17 No. 1 (2023): January 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this research is to create a batik motive image classification system to make it easier for the public to know the name of a type of batik motive. In carrying out this research, a quantitative method was used with seven kinds of batik motives that were augmented first, where 70% of the dataset was used for training and 30% for testing so that the accuracy and precision of the system were obtained. The result of this research is that the accuracy and precision of the system in classifying batik motive images is 0.985 or 98.5%. This high accuracy and precision were obtained because the quality of the previous dataset was improved by augmenting geometric and photometric. The machine learning method used was a Convolutional Neural Network which in previous studies also provided the highest accuracy and precision. The results of this study can be used for various purposes such as marketing, cultural reservation, and science.
SUPPORT VECTOR MACHINE TO CLASSIFY SENTIMENT REVIEWS ON GOOGLE PLAY STORE Nursikuwagus, Agus; Suherman; Purwanto, Heri; Hartono, Tono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7282

Abstract

This research addresses the "rating-content discrepancy" on the Google Play Store, where numerical star ratings often conflict with the actual sentiment of textual reviews. Utilizing the CRISP-DM   framework, the study evaluates the effectiveness of machine learning in resolving these inconsistencies by classifying Instagram user reviews into positive and negative categories. Two primary algorithms were compared using a dataset of 600 reviews. The Support Vector Machine (SVM) model demonstrated high efficacy with an accuracy of 0.84. In contrast, the K-Nearest Neighbors (KNN) model performed poorly, achieving an accuracy of only 0.48. This significant performance gap highlights SVM's superior ability to handle high-dimensional text data through optimal hyperplane separation. The research further integrated the Streamlit library to create an interactive web interface for real-time sentiment prediction and result visualization. Ultimately, this study confirms that a structured CRISP-DM approach combined with SVM provides a robust solution for automated opinion mining, offering a reliable methodology for future data science applications in social media analysis
Co-Authors Choir, M M Dadang Munandar Dimitri Mahayana Djamaludin, Irsyad Fitriawati, Mia Hendry Gunawan, Hendry Heri Purwanto Hikmawati, Erna Ilham Alamsyah Mauluddin, Syahrul Munggana, Wira Nurwicaksono, M A Saputri, M A Sidik, Rangga Siti Kurnia Rahayu Suherman Suherman, Suherman Syahrul Mauluddin Taufik Juliana, Taufik Tono Hartono, Tono Umi Narimawati Untari Novia Wisesty