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TEXT MINING UNTUK KLASIFIKASI PENGADUAN PADA SISTEM LAPOR MENGGUNAKAN METODE C4.5 BERBASIS FORWARD SELECTION Ali Sofyan; Stefanus Santosa
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 1 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Report of public complaints on the site becomes a medium of communication between the community and government agencies. The number of incoming documents every day be a source of information to measure the services of government agencies. Classification of documents is very important to do otherthan to ensure that the intended objectives of the institution, as well as to classify complaints fit the category. C4.5 algorithm is one of the algorithms that can be used for classification. There were some complaints classification research. This study aims to apply the classification of complaints by algritma C4.5 with a selection of features to improve the accuracy of classification. Results of experiments with methods of research division of the number of datasets, cross validation, classification with and without features. The test results obtained by testing the value of the best accuracy with 550 documents with forward selection, with cross valiadtion 9folds with a value of 85.27%. precission 87.8% and 85.3% recall
Perancangan Aplikasi E-Commerce Sentra Warga Rizky Nur Hidayat; Ali Sofyan
urn:multiple://2988-7828multiple.v3i14
Publisher : Institute of Educational, Research, and Community Service

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Abstract

Dalam era digital yang terus berkembang, UMKM, termasuk toko kelontong di tingkat RT dan RW, menghadapi tantangan untuk memasarkan produk mereka secara lebih luas. Pengembangan aplikasi e-commerce yang inovatif dan efisien menjadi semakin penting untuk membantu UMKM bersaing di pasar global. Aplikasi e-commerce memungkinkan bisnis mengatasi batasan geografis, menjangkau pasar internasional, dan memberikan pengalaman belanja yang lebih nyaman bagi pelanggan.Pembuatan situs e-commerce ini menggunakan teknik analisis data dengan metode pengembangan perangkat lunak Agile. Sistem pembayaran yang tersedia mencakup transfer antar rekening bank dan pembayaran di tempat (COD). Tujuan utama dari pengembangan sistem ini adalah memudahkan pelanggan dalam memesan barang tanpa harus datang langsung ke toko serta membantu pengelolaan produk dengan lebih efisien. Berdasarkan hasil pengujian, secara fungsional sistem telah mampu memenuhi kebutuhan yang diharapkan meskipun belum sepenuhnya sempurna. Aplikasi ini dinilai mudah digunakan, memiliki tampilan yang cukup menarik, dan mampu mempercepat pengolahan data.
Analisis Kinerja Jaringan Internet Dalam Mendukung Pelayanan Di Dinas Sosial Kabupaten Brebes Nafi’z Al Ai’shyah Tazak’ka; Ali Sofyan
Sinergi : Jurnal Ilmiah Multidisiplin Vol. 1 No. 2 (2025): Sinergi: Jurnal Ilmiah Multidisiplin
Publisher : PT. AHLAL PUBLISHER NUSANTARA

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Abstract

The development of information and communication technology (ICT) plays an important role in supporting the effectiveness and efficiency of work, particularly in public services within the government sector. One of the key technologies supporting these activities is the internet network. This study aims to analyze the performance of the internet network at the Social Services Office of Brebes Regency, which is used in various activities such as poverty data collection, submission and disbursement of social assistance (bansos), and other administrative services. Based on observations and tests, it was found that the existing internet network has not been able to optimally meet operational needs. Slow access speeds, unstable connections, and outdated network devices are the main factors hindering service delivery. This condition causes delays in data entry, communication disruptions between agencies, and a decline in public satisfaction with the services provided. The study also identifies the lack of bandwidth management and insufficient device maintenance as additional causes. To address these issues, improvements in bandwidth capacity, replacement of network devices, implementation of a monitoring system, and IT staff training are necessary. Through these measures, it is expected that the Social Services Office can improve the quality of public services to be faster, more efficient, and accountable. This study contributes in the form of technical recommendations to support the optimal implementation of e-Government at the regional level. Keywords: Internet Network, Cisco, Bandwidth, and Information Technology.   Abstrak Perkembangan teknologi informasi dan komunikasi (TIK) memiliki peran penting dalam mendukung efektivitas dan efisiensi kerja, terutama dalam pelayanan publik di sektor pemerintahan. Salah satu teknologi utama yang menunjang kegiatan tersebut adalah jaringan internet. Penelitian ini bertujuan untuk menganalisis kinerja jaringan internet di Dinas Sosial Kabupaten Brebes, yang digunakan dalam berbagai aktivitas seperti pendataan warga miskin, pengajuan dan pencairan bantuan sosial (bansos), serta layanan administrasi lainnya. Berdasarkan hasil observasi dan pengujian, ditemukan bahwa jaringan internet yang ada belum mampu memenuhi kebutuhan operasional secara optimal. Kecepatan akses yang lambat, koneksi yang tidak stabil, serta perangkat jaringan yang sudah usang menjadi faktor utama yang menghambat pelayanan. Kondisi ini menyebabkan keterlambatan dalam penginputan data, gangguan komunikasi antarinstansi, dan menurunnya kepuasan masyarakat terhadap layanan yang diberikan. Penelitian ini juga mengidentifikasi minimnya manajemen bandwidth dan kurangnya pemeliharaan perangkat sebagai penyebab tambahan. Untuk mengatasi permasalahan tersebut, diperlukan peningkatan kapasitas bandwidth, penggantian perangkat jaringan, implementasi sistem monitoring, serta pelatihan staf IT. Melalui langkah-langkah tersebut, diharapkan Dinas Sosial dapat meningkatkan kualitas layanan publik yang lebih cepat, efisien, dan akuntabel. Penelitian ini memberikan kontribusi dalam bentuk rekomendasi teknis untuk mendukung penerapan e-Government secara optimal di tingkat daerah. Kata Kunci : Jaringan Internet, Cisco, Bandawitch, dan Teknologi Informasi.
Analisis Kinerja Algoritma C4.5 pada Dataset Titanic yang Tidak Seimbang Menggunakan Gain Ratio: Penelitian Kuncoro Singgih Prasojo; Hasbi Firmansyah; Wahyu Asriyani; Ali Sofyan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.4402

Abstract

This study aims to analyze the performance of the C4.5 algorithm in classifying passenger survival status using the Titanic dataset, which exhibits an imbalanced class distribution. The research employed a quantitative approach consisting of data preprocessing, manual calculation of entropy, information gain, split information, and gain ratio using Microsoft Excel, followed by model implementation using RapidMiner. The dataset contains 800 passenger records with the survived attribute defined as the class label. Manual calculation results indicate that the Gender attribute has the highest information gain value of 0.955, making it the root node of the decision tree, while other attributes such as Pclass, Age Group, and Fare Group contribute very limited information. The experimental results show that the C4.5 model achieves an accuracy of 62.50%; however, all test instances are predicted as non-survived, resulting in 0% precision and recall for the survived class. In addition, the generated decision tree structure is very shallow with no significant branching. These findings demonstrate that class imbalance in the Titanic dataset strongly affects the performance of the C4.5 algorithm, indicating the need for imbalanced data handling techniques to improve classification results.
Prediksi Unit Price Properti Menggunakan Algoritma Neural Network Berbasis RapidMiner: Penelitian Bimo Aryo Pangestu; Hasbi Firmansyah; Ali Sofyan; Wahyu Asriyani
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.4439

Abstract

This study aims to predict property unit price using the Neural Network algorithm based on RapidMiner. The dataset used consists of property-related attributes, with unit price as the target variable. The research stages include attribute role assignment, data normalization, and data partitioning using the estimation method with a 70:30 split between training and testing data. The Neural Network model is built using the training data and applied to the testing data to generate unit price predictions. Model performance is evaluated using the Performance (Regression) method with the Root Mean Squared Error (RMSE) metric. The experimental results show that the Neural Network algorithm is able to predict property unit price accurately, as indicated by an RMSE value of 0.028. The low RMSE value indicates a small difference between the actual and predicted unit price values, demonstrating that the proposed model has good predictive performance. Therefore, it can be concluded that the Neural Network algorithm based on RapidMiner is effective for predicting property unit priprice and can be used as an alternative approach in property price analysis.
Segmentasi Pelanggan Grosir Menggunakan K-Means: Analisis Outlier dan Ketidakseimbangan Data : Penelitian N Tahta Phudjashakty; Hasbi Firmansyah; Wahyu Asriyani; Ali Sofyan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4771

Abstract

This study aims to segment wholesale customers using the K-Means clustering algorithm and to examine the impact of outliers and data imbalance on the clustering results. The data are taken from the Wholesale Customers Dataset of the UCI Machine Learning Repository, consisting of 440 customers with eight numerical attributes representing annual purchase amounts. The preprocessing steps include exploratory data analysis, outlier detection using Z-Score and boxplot visualization, handling of extreme values with winsorizing, and Z-Score normalization to make the attribute scales comparable. The number of clusters is determined using the Elbow Method. Applying K-Means with produces two highly imbalanced clusters, with 437 customers in Cluster 0 and 3 customers in Cluster 1. Cluster 0 represents regular customers whose purchasing patterns are close to the overall average, while Cluster 1 consists of customers with very high purchases, especially in Frozen and Delicassen categories. Evaluation using the average within centroid distance and the Davies–Bouldin Index shows that, after outlier handling and normalization, the cluster structure becomes more stable and easier to interpret. The resulting segmentation can support differentiated marketing and service strategies for regular and high-spending customers and highlights the importance of proper preprocessing when applying K-Means.