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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sintaks Logika (JSilog)
Bambang Irawan
Teknik Informatika, Universitas Muhadi Setiabudi
Computer Science & IT

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ALGORITMA NAIVE BAYES UNTUK MENGKLASIFIKASIKAN STUNTING PADA ANAK BALITA BERBASIS WEBSITE Lutfi Afifah; Otong Saeful Bachri; Bambang Irawan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.17078

Abstract

Stunting merupakan permasalahan gizi kronis menjadi tantangan serius di Indonesia, khususnya pada balita. Kondisi disebabkan oleh kekurangan gizi dalam jangka panjang yang dimulai sejak masa kehamilan hingga usia dua tahun. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi kejadian stunting pada balita. Metode penelitian yang digunakan adalah pendekatan deskriptif dengan pengumpulan data melalui observasi, wawancara, dan studi dokumentasi pada balita dan orang tua. Hasil penelitian menunjukkan bahwa stunting dipengaruhi oleh asupan gizi yang tidak adekuat, rendahnya pengetahuan orang tua tentang gizi, riwayat penyakit infeksi, serta kondisi sanitasi lingkungan yang kurang baik. Kesimpulan dari penelitian ini menegaskan bahwa upaya pencegahan stunting memerlukan intervensi terpadu melalui perbaikan gizi, peningkatan pola asuh, serta penguatan layanan kesehatan dan sanitasi lingkungan. Stunting, yang merupakan kondisi malnutrisi dan hambatan pertumbuhan, ditandai dengan tinggi badan yang tidak sesuai untuk usia. Penelitian ini bertujuan untuk mengembangkan sistem berbasis web untuk penentuan stunting pada anak di Satu Pintu Layanan Data (SAPULADA) Kabupaten Brebes. Atribut yang digunakan dalam penelitian ini meliputi NIK, nama, tanggal lahir, tinggi badan, dan berat badan, dengan 1500 data balita dari posyandu. Hasil penelitian menunjukkan bahwa sistem ini mencapai akurasi tinggi dalam pengklasifikasian, dengan persentase stunting sebesar 75%, pra- stunting sebesar 45%, dan status normal sebesar 30%.
Pemodelan Analisis Sentimen Ulasan Pengguna Aplikasi Info Bmkg Menggunakan Pendekatan Multinomial Naïve Bayes Moh. Syaogi; Nur Ariesanto Ramdhan; Otong Saeful Bachri; Bambang Irawan
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4285

Abstract

Info BMKG is one of several digital platforms that have been pushed by the fast evolution of IT to replace traditional methods of providing public services. Reviews on the Play Store can be used to determine user perceptions and levels of satisfaction with the application. Manual analysis is laborious and inefficient due to the high number of evaluations. Consequently, the purpose of this research is to use the Naive Bayes algorithm to categorize evaluations of the Info BMKG app as either positive or negative in order to do sentiment analysis. Using a web scraping approach, a total of 5,000 user evaluations were obtained for the study data. Next, the data underwent text preprocessing, word weighting using the TF-IDF technique, and sentiment classification with the Multinomial Naive Bayes algorithm. There was an 80:20 split between the dataset's training and testing sets. The experimental findings show that the Naive Bayes algorithm achieves an accuracy of 87.83% on the testing data when it comes to classifying user review emotions.
Prediksi Kanker Payudara Berbasis Machine Learning Dengan Analisis Probabilitas Klasisfikasi luthfi ardyansyah; Bambang Irawan
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4294

Abstract

Breast cancer is one of the diseases with a high mortality rate in women, so early detection is crucial to increase the chances of recovery. Unfortunately, conventional methods of diagnosis still rely on the interpretation of medical personnel and laboratory procedures which are time-consuming and costly. This study tries to present a machine learning-based approach to predict breast cancer, while adding a classification probability analysis to make the prediction more informative. The breast cancer dataset was used to train four models, namely Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbor. Evaluation was carried out using accuracy, confusion matrix, ROC curve, and AUC. The results showed that all four models were able to classify cancers with fairly high performance, while one model stood out with the highest accuracy and AUC values. Classification probability analysis provides additional perspective on the confidence level of predictions, which can help medical personnel make more objective clinical decisions.
Analisis Pola Konsumsi Energi Listrik Pelanggan Rumah Tangga Menggunakan Alogaritma K-Means Clustering Hilmi Mubarok; Bambang Irawan
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4296

Abstract

The increase in household electricity consumption is one of the main challenges in national energy management. Diverse electricity usage patterns are influenced by social, economic, and behavioral characteristics of consumers. This study aims to analyze and cluster household electricity consumption patterns using the K-Means Clustering algorithm. The dataset consists of secondary data from 1,200 household customers with attributes including installed power capacity, monthly electricity consumption (kWh), peak usage time, and average daily load. The research stages include data cleaning, normalization using StandardScaler, determination of the optimal number of clusters using the Elbow Method, clustering with K-Means, and evaluation using the Davies-Bouldin Index (DBI). The results indicate that the optimal number of clusters is three, representing low, medium, and high electricity consumption groups. A DBI value of 0.71 indicates good clustering quality. These findings can support electricity providers in designing energy efficiency policies and household load management strategies.
Co-Authors Hilmi Mubarok Lutfi Afifah luthfi ardyansyah Moh. Syaogi Nur Ariesanto Ramdhan Otong Saeful Bachri