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Irawan, Bambang
Program Studi Informatika , Fakultas Teknik, Universitas Muhadi Setiabudi, Indonesia
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ANALISIS KINERJA CONVOLUTIONAL NEURAL NETWORK DALAM KLASIFIKASI MASA PAKAI SEPATU LARI BERDASARKAN POLA PENGGUNAAN Inesya Melodifa; 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.v10i1.16966

Abstract

Perkembangan metode deep learning, khususnya Convolutional Neural Network (CNN), membuka peluang penerapannya tidak hanya pada pengolahan citra, tetapi juga pada klasifikasi data kategorikal yang kompleks. Dalam konteks olahraga lari, penentuan masa pakai sepatu lari masih bersifat subjektif dan belum didukung oleh pendekatan berbasis data yang sistematis. Oleh karena itu, penelitian ini bertujuan untuk menerapkan metode CNN dalam mengklasifikasikan masa pakai sepatu lari berdasarkan variabel penggunaan seperti frekuensi pemakaian, jenis medan lari, tingkat kecepatan, jarak tempuh, dan persepsi performa sepatu. Penelitian ini menggunakan pendekatan kuantitatif dengan pengumpulan data melalui kuesioner, yang kemudian diproses melalui tahap seleksi, pembersihan data, pelatihan model CNN, serta evaluasi menggunakan confusion matrix dan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model CNN mampu mengklasifikasikan data dengan tingkat akurasi sebesar 75%, dengan performa terbaik pada kategori dengan jumlah data dominan. Temuan ini menunjukkan bahwa CNN berpotensi menjadi pendekatan yang efektif untuk mendukung pengambilan keputusan terkait masa pakai sepatu lari secara objektif dan berbasis data
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%.
Analisis Sentimen Ulasan Wisata Alun-Alun Brebes pada Google Maps Menggunakan Support Vector Machine Azkiyatul Maulida; Bambang Irawan; Nur Ariesanto Ramdhan
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8883

Abstract

The rapid development of information technology has encouraged the use of digital platforms as media for sharing opinions, including tourism reviews on Google Maps. Alun-Alun Brebes, as one of the most frequently visited public spaces, has generated thousands of reviews with diverse textual characteristics, making manual analysis inefficient and impractical. This study aims to analyze visitor sentiment toward Alun-Alun Brebes by applying a text mining approach using the Support Vector Machine algorithm. The dataset consists of 1,000 Google Maps reviews, including 327 reviews manually labeled as positive and negative sentiments and 673 unlabeled reviews. The research stages include data collection, text preprocessing, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, Support Vector Machine model training and testing, and automatic labeling of unlabeled data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics based on the manually labeled data. The results show that the Support Vector Machine model with a linear kernel achieved an accuracy of 100% with an F1-score of 1.00, indicating excellent sentiment classification performance. Furthermore, word cloud visualization reveals that positive sentiment is dominated by aspects related to comfort and facilities, while negative sentiment is associated with cleanliness, crowd density, and environmental management. These findings provide data-driven insights into key aspects that should be maintained and improved in managing Alun-Alun Brebes as a public space.
Sentiment Analysis of Shopee User Reviews Using Recurrent Neural Network with LSTM for Real-Time Web-Based Prediction Suci Ayu Qurani; Bambang Irawan; Nur Ariesanto Ramdhan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Articles Research Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7824

Abstract

Sentiment analysis has become an important approach for understanding user opinions on e-commerce platforms. Shopee user reviews provide valuable information that can be utilized to evaluate service quality and customer satisfaction. This study aims to analyze the sentiment of Shopee user reviews using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) architecture. The research method includes data collection, text preprocessing, model training, and performance evaluation. The experimental results show that the proposed RNN-LSTM model achieved an accuracy of 97%, indicating its effectiveness in classifying user sentiment. The developed model is further implemented in a web-based application to provide real-time sentiment prediction. The findings of this study demonstrate that the RNN-LSTM approach is suitable for sentiment analysis in e-commerce environments and can support decision-making based on user feedback.
PENERAPAN SISTEM PENDUKUNG KEPUTUSAN MENGGUNAKAN METODE TOPSIS UNTUK PENERIMA BANTUAN PUPUK SUBSIDI Muhammad Zaqi Mubarok; Bambang Irawan; Otong Saeful Bachri
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.5288

Abstract

Program bantuan pupuk subsidi NPK 15-15-15 di Kecamatan Adiwerna, Kabupaten Tegal, masih menghadapi kendala pada proses seleksi penerima yang dilakukan secara manual dan subjektif, sehingga berpotensi menimbulkan ketidaktepatan sasaran. Penelitian ini bertujuan merancang Sistem Pendukung Keputusan (SPK) berbasis metode TOPSIS untuk meningkatkan objektivitas, akurasi, dan transparansi dalam penentuan penerima bantuan pada kelompok tani. Metode TOPSIS diterapkan melalui tahapan penyusunan matriks keputusan, normalisasi, pembobotan berdasarkan lima kriteria (komoditas, tahun pembentukan, jumlah anggota, kelas, dan luas lahan), penentuan solusi ideal positif dan negatif, perhitungan jarak, serta penentuan nilai preferensi. Hasil penelitian menunjukkan bahwa kelompok tani dengan nilai preferensi tertinggi mencapai 0,82 sehingga dinilai paling layak, sedangkan nilai terendah 0,31 berada pada prioritas akhir penerimaan bantuan. Sistem ini dibangun berbasis web menggunakan PHP dan MySQL, serta dilengkapi diagram UML dan rancangan antarmuka untuk administrator, petugas, dan pengguna. Implementasi SPK ini meningkatkan efisiensi penyaluran bantuan pupuk subsidi dan mendukung pengambilan keputusan yang lebih terukur bagi kelembagaan pertanian di wilayah Adiwerna.
PENERAPAN METODE ANALYTICAL HIERARCHY PROCESS UNTUK PENILAIAN TENAGA KESEHATAN KOTA TEGAL Dzaky Abdul Ghaffar; Otong Saeful Bachri; Bambang Irawan
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.5292

Abstract

Penilaian kinerja tenaga kesehatan berperan penting dalam menjaga kualitas pelayanan kesehatan. Namun, penilaian manual sering kali subjektif dan kurang terstruktur, sehingga dapat memengaruhi objektivitas keputusan. Penelitian ini bertujuan merancang Sistem Pendukung Keputusan (SPK) berbasis metode Analytical Hierarchy Process (AHP) untuk menghasilkan evaluasi kinerja yang objektif dan terukur. Sistem ini diterapkan di Dinas Kesehatan Kota Tegal dengan sembilan kriteria penilaian berdasarkan nilai-nilai ASN BerAKHLAK dan indikator profesionalisme. Data dikumpulkan melalui observasi, wawancara, dan dokumentasi yang melibatkan 83 tenaga kesehatan. Metode AHP digunakan untuk menentukan bobot kriteria melalui perbandingan berpasangan, menghitung rasio konsistensi, dan menghasilkan peringkat akhir pegawai. Hasil penelitian menunjukkan bahwa SPK ini efektif dalam memproses data penilaian dan menghasilkan peringkat yang akurat. Sistem ini mendukung pengambilan keputusan terkait promosi, penghargaan, dan pengembangan kompetensi secara transparan dan objektif. Pengembangan di masa depan dapat mencakup integrasi dengan sistem manajemen kepegawaian dan fitur pelaporan otomatis.
Analisis Analisis Sentimen Ulasan eFootball pada Google Play Store Menggunakan Multinomial Naive Bayes dan Support Vector Machine Mohammad Umar Sasongko Sasongko; 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.v6i1.4282

Abstract

In the digital era, the use of mobile applications is increasing, so it is important to understand user satisfaction and dissatisfaction with the applications used. One of the popularmobile games in Indonesia is eFootball 2026, which has a lot of reviews from players on the Google Play Store. The large number of reviews allows sentiment analysis to be carried out to find out user opinions on the quality of the application. This study aims to analyze the sentiment of eFootball application user reviews using the Multinomial Naive Bayes method and Support Vector Machine. Review data is processed through the stages of text preprocessing, feature extraction using TF-IDF, and class imbalance handling with SMOTE. Model evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the Multinomial Naive Bayes method produced an accuracy of 76.72%, while the Support Vector Machine obtained an accuracy 74.92%, with relatively balanced precision, recall, and F1-score values. Based on these results, it can be concluded that the Multinomial Naive Bayes method has a better performance in analyzing the sentiment of eFootball app reviews on the Google Play Store and can be used as a basis for evaluation for future app development.
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 Azkiyatul Maulida Dzaky Abdul Ghaffar Hilmi Mubarok Inesya Melodifa Lutfi Afifah luthfi ardyansyah Moh. Syaogi Mohammad Umar Sasongko Sasongko Muhammad Zaqi Mubarok Nur Ariesanto Ramdhan Nur Ariesanto Ramdhan Nur Ariesanto Ramdhan Otong Saeful Bachri Otong Saeful Bachri Otong Saeful Bachri santowi azis Suci Ayu Qurani Wahyu Riski Maulana