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All Journal Jurnal Simetris Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Sistem Pendukung Keputusan dengan Aplikasi
Ramadhanu, Ramadhanu
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Metode Machine Learning untuk Klasifikasi Data Gizi Balita dengan Algoritma Naïve Bayes, KNN dan Decision Tree Ramadhani, Ramadhani; Ramadhanu, Ramadhanu
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 15, No 1 (2024): JURNAL SIMETRIS VOLUME 15 NO 1 TAHUN 2024
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i1.10679

Abstract

Stunting in toddlers is a serious health problem, Stunting is a term used to describe the delay in physical development of children from conception or formation to the age of 2 years, resulting in height lower than their chronological age. Stunting in toddlers can be caused by socioeconomic conditions, maternal nutrition during pregnancy, infant diseases, and inadequate infant nutritional intake. Infectious diseases are the most direct and common cause of growth failure in young children, and effective strategies are needed to reduce risk factors for developmental delays in children under the age of five. The method to overcome this problem is a machine learning (ML) classification method that uses Naive Bayes, KNN and Decision Tree algorithms to classify nutritional data of young children, thus helping to overcome developmental delays, early intervention. The result of this study is the highest precision poor naïve bayes algorithm performance found in the malnutrition category at 38% and recall there are two categories that cannot be identified. The KNN algorithm has one category of nutritional risk that cannot be identified precision and recall, KNN is higher than naïve bayes at 40%. The Decision Tree looks normal and has 48% accuracy, with better recall and precision than Naive Bayes and KNN
Studi Komparatif Multinomial Naïve Bayes, Decision Tree, dan K-Nearest Neighbor dalam Klasifikasi Validasi Ulasan Clash of Clans oleh Pengguna Ahli Ramadhani, Ramadhani; Ramadhanu, Ramadhanu; Ridwan, Mohammad; Abdillah, Amrullah Fajri Artha; Hidayat, Taufik
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

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

Abstract

User reviews of the app, including popular games like Clash of Clans, are an important indicator of the quality of the app. However, fake reviews can mislead users and damage the app's reputation. This study aims to classify Clash of Clans reviews on the Google Play Store as "valid" or "invalid" using three algorithms: decision tree (DT), K-Nearest Neighbors (KNN), and Multinomial Naive Bayes (MNB). The dataset used contains 320 Indonesian reviews with a rating of 1 to 3 and validated by experienced players. Text features are extracted using TF-IDF and the model is evaluated using cross validation. The results show that the decision tree has the highest accuracy (64%), followed by MNB (59%) and KNN (53%). Cross validation shows that MNB has the most stable performance, while KNN is more sensitive to data changes. Decision Trees show the lowest performance and are less effective on new data because they tend to overfit. The study provides valuable insights into the selection of user review classification algorithms by considering accuracy, precision, acquisition, and performance stability.
Optimasi Rute Terpendek pada Objek Wisata di Kabupaten Tangerang Menggunakan Algoritma Genetika dengan Pendekatan Travelling Salesman Problem Ramadhani, Ramadhani; Ramadhanu, Ramadhanu; Fiddin, Fahmi
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 1 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i1.1125

Abstract

Tangerang Regency has numerous tourist destinations spread across various locations. However, tourists often face difficulties in determining an efficient travel route due to traffic congestion and irregular distances between sites. This issue leads to suboptimal travel time and reduces the overall comfort of the tourism experience. This study aims to optimize tourism travel routes in Tangerang Regency using a genetic algorithm approach based on the Travelling Salesman Problem (TSP). Data were collected from 17 tourist attractions, including their geographical coordinates, and processed through several genetic algorithm stages: population initialization, selection, crossover, and mutation. The results show that the genetic algorithm successfully produced an optimal route with a total distance of 109.77 km and the best fitness value of 0.009110. Compared to the initial distance before optimization, which was 215.80 km, this result indicates a travel distance efficiency improvement of 49.15%. These findings suggest that the genetic algorithm approach provides an effective solution for tourism route planning. The results are expected to serve as a basis for developing tourism promotion strategies and improving infrastructure in Tangerang Regency.
KLASIFIKASI SENTIMEN PENGGUNA PADA ULASAN APLIKASI E-WALLET DI GOOGLE PLAY STORE: PENDEKATAN MACHINE LEARNING Ramadhanu, Ramadhanu; ramadhani, ramadhani
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 4 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i4.94761

Abstract

Pertumbuhan aplikasi e-wallet di Indonesia mendorong pentingnya analisis sentimen untuk memahami ulasan pengguna secara otomatis. Penelitian ini bertujuan mengklasifikasikan sentimen ulasan aplikasi e-wallet di Google Play Store dengan membandingkan kinerja tiga algoritma machine learning: Extra Trees, Passive Aggressive, dan Ridge Classifier. Sebanyak 47.626 ulasan dikumpulkan melalui web scraping dan diberi label menggunakan InSet Lexicon. Data diproses melalui tahap case folding, cleaning, stopword removal, dan lemmatization. Fitur diekstraksi menggunakan TF-IDF dan diseleksi dengan Chi-Square. Optimasi hyperparameter dilakukan menggunakan Bayesian Optimization dengan pustaka Optuna, dan evaluasi dilakukan dengan Stratified 5-Fold Cross-Validation. Hasil menunjukkan bahwa Passive Aggressive Classifier memiliki kinerja terbaik dengan akurasi 95,08%, precision 92,97%, recall 89,47%, dan F1-score 90,91% pada data uji. Model ini unggul dalam hal stabilitas dan akurasi dibandingkan dua algoritma lainnya. Temuan ini menunjukkan bahwa model yang efisien dan adaptif terhadap data berdimensi tinggi sangat sesuai untuk klasifikasi sentimen teks dalam Bahasa Indonesia. Penelitian ini memberikan kontribusi dalam pengembangan sistem analisis opini pengguna untuk peningkatan kualitas layanan aplikasi digital.
Exploratory Data Analysis (EDA) untuk Mengetahui Distribusi Data Kualitas Susu Sapi Ramadhani, Ramadhani; Ramadhanu, Ramadhanu; Hidayat, Taufik
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 23 No 1 (2024): Februari 2024
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v23i1.9500

Abstract

Kesehatan dan kesejahteraan hewan sangat penting dalam peternakan, dan kesehatan serta kesejahteraan hewan sangat bergantung pada cara manusia mengelola hewan. Susu merupakan minuman yang memiliki banyak manfaat karena mengandung banyak nutrisi penting seperti berbagai vitamin, protein, kalsium, dan magnesium. Untuk memperoleh susu yang berkualitas, susu harus diolah dengan baik. Oleh karena itu, pengendalian mutu susu diperlukan untuk memperoleh produk susu yang berkualitas, dan jika susu terkontaminasi dapat berbahaya atau menimbulkan gangguan kesehatan. Susu dapat dipengaruhi oleh banyak faktor seperti kesehatan, pakan, penanganan sapi, interval pemerahan, dan lingkungan. Solusi dari permasalahan tersebut adalah dengan metode exploratory data analysis (EDA), yaitu metode analisis data yang memungkinkan untuk memahami sebaran data, dan menggunakan metode visualisasi data dengan menggunakan grafik batang. Hasil penelitian ini adalah distribusi data Warna, Suhu, Ph. Perbandingan antar sample mayoritas dan minoritas sangat jauh. Sedangkan distribusi data yang lain masih normal antara sample mayoritas dan minoritas.
Co-Authors AA Sudharmawan, AA Abdillah, Amrullah Fajri Artha Fiddin, Fahmi Ramadhani Ramadhani Taufik Hidayat