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All Journal Jurnal PAI Raden Fatah JSAI (Journal Scientific and Applied Informatics) Jurnal Informasi dan Teknologi Jurnal Software Engineering and Computational Intelligence
Evi Purnamasari
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Religion Humanities Computer Science & IT Education Social Sciences

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Prediction Analysis of Customer Satisfaction Levels at Company XXX Using the Classification Method Evi Purnamasari; Priscila Yuni Praditya, Ni Wayan; Dwi Asa Verano
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i2.541

Abstract

Service in companies operating in the service system plays a very important role, including in one of the companies in the city of Palembang which we call Company XXX. The level of customer satisfaction with service at Company XXX needs to be considered in order to find out how satisfied customers are with the service system provided by Company XXX. On this occasion the researcher aims to analyze and predict the level of customer satisfaction at Company XXX using the C4.5 classification method. Customer satisfaction is an important factor in maintaining customer loyalty and improving company performance. Using historical customer data for the last 1 year, we apply the C4.5 algorithm to predict customer satisfaction levels. The research results show that the C4.5 method has quite high prediction accuracy, which reaches 83%. It is hoped that the findings from this research can help XXX Company identify the factors that influence customer satisfaction and be able to take strategic steps to improve the quality of service.
Pendekatan Data-Driven untuk Pengembangan Model Prediksi Tingkat Kemiskinan di Provinsi Indonesia Evi Purnamasari; Dwi Asa Verano
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7596

Abstract

Poverty in Indonesia remains a major issue that requires serious attention, particularly at the provincial level. Various factors, such as access to education, healthcare, and employment opportunities, affect the poverty rate. This study aims to develop a poverty prediction model using a data-driven approach through cluster analysis and classification. The methods used in clustering are K-Means, Hierarchical Clustering, and DBSCAN, while for classification, the algorithms applied are Random Forest, Naive Bayes, and Support Vector Machine (SVM). The clustering analysis results show that K-Means provides clearer cluster divisions with the highest Calinski-Harabasz Index value (179.45). In classification model testing, Naive Bayes provides the best results with an accuracy of 99.42%, which is higher than the other models. To address overfitting, cross-validation testing was conducted, yielding a Mean Accuracy of 99.32% and a Standard Deviatin of 0.23%. This study successfully identifies the factors influencing poverty levels in Indonesia’s provinces, which can be used as a basis for government policies in poverty alleviation efforts. The results achieved contribute significantly to the development of a more accurate and effective predictive model for addressing poverty issues in Indonesia.
Edukasi Tentang Pengenalan Perangkat Keras dan Perangkat Lunak Pada Anak Usia Dini Evi Purnamasari; Dwi Asa Verano
PAI RAFAH Vol 7 No 1 (2025): Jurnal PAI Raden Fatah
Publisher : Program Studi Pendidikan Agama Islam Fakultas Ilmu Tarbiyah Dan Keguruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/pairf.v7i1.27226

Abstract

Education about hardware and software is very important to improve technological literacy. This study discusses the introduction of hardware components such as CPU, RAM, motherboard, mouse, keyboard, hard disk, and software such as operating systems and applications. Interactive technology-based learning methods are used to facilitate understanding. This study uses a qualitative approach with a descriptive analysis method to describe the application of observation and question and answer methods as education for introducing hardware and software to early childhood. Data were collected through structured interviews, participatory observation and document analysis. Data obtained through observation and interviews were analyzed qualitatively. To test the validity of the data, data triangulation was used, namely comparing the findings of observations, interviews and document analysis. The results of the study showed that this approach significantly increased students' interest and knowledge in learning. Practice-based learning has proven to be the key to improving students' technological literacy. In addition, increasing technological literacy is expected to help students be better prepared to face challenges in the digital era.
Prediksi Kepuasan Pelanggan pada Layanan E-government Menggunakan Algoritma Decision Tree Indah Permatasari; Dona Marcelina; Evi Purnamasari
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7718

Abstract

The Online Licensing Service Information System (SIPPERI) implemented by DPMPTSP Palembang City aims to enhance efficiency, transparency, and accountability in public services. However, several challenges were reported by users, including unintuitive navigation, slow system responses, and inaccurate information. These challenges impact the level of user satisfaction with the service. This study uses the decision tree algorithm to evaluate user satisfaction based on data obtained through questionnaires with a Likert scale assessment involving 100 respondents. The analysis process uses the Python programming language. The dimensions analyzed include Efficiency (E), Trust (T), Reliability (R), Service (CS), Usability (U), Information Availability (I), and Interaction (SI). The analysis results show that the decision tree algorithm achieves an accuracy rate of 95%. The highest-scoring dimensions were recorded in the indicators Download Speed of Forms (R1: 392) and Accuracy of Instructions (E4: 392). Conversely, the lowest-scoring dimensions were Intuitive Navigation (E1: 300) and Information Availability (I1: 314). This study provides strategic recommendations for DPMPTSP Palembang City to improve dimensions with low scores to enhance user experience and strengthen public trust in e-government services.
Prediksi Penjualan Produk Pada PT Bintang Sriwijaya Palembang Menggunakan K-Nearest Neighbour: Prediksi Calon Mahasiswa Penerima KIP Pada Universitas Indo Global Mandiri menggunakan Algoritma Decision Tree Miftahul Jannah; haviz, Muhammad haviz irfani; Dewi Sartika; Evi Purnamasari
Jurnal Software Engineering and Computational Intelligence Vol 1 No 2 (2023)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v1i2.3542

Abstract

Penjualan merupakan faktor terpenting bagi seluruh perusahaan karena dengan adanya pnejualan, maka suatu perusahaan akan mendapat keuntungan yang lebih supaya bisa melanjutkan usaha tersebut. Prediksi atau peramalan penjualan (forecasting) adalah suatu perhitungan untuk meramalkan keadaan di masa mendatang melalui pengujian keadaan di masa lalu. Tujuan penelitian untuk memberikan usulan kepada perusahaan dalam menentukan stok barang berdasarkan prediksi data penjualan sebelumnya dengan menggunakan metode K-Nearest Neighbor (KNN). Berdarkan hasil penelitian yang telah dilakukan dapat diambil kesimpulan bahwa hasil dari perhitungan menggunakan algoritma KNN, didapatkan hasil prediksi penjualan produk berdasarkan nilai akurasi tertinggi dan terendah. Nilai akurasi tertinggi terhadap penjualan produk sebesar 97,3%. Sedangkan nilai akurasi terenda terhadap penjualan produk sebesar 86,5%. Dengan demikian metode algoritma KNN k=20 (97,3%) ini dapat diimplementasikan untuk memprediksi penjualan produk PT Bintang Sriwijaya Palembang.
Co-Authors Dewi Sartika Dona Marcelina Dwi Asa Verano haviz, Muhammad haviz irfani Indah Permatasari MIFTAHUL JANNAH Priscila Yuni Praditya, Ni Wayan