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Kharisma, Nanda
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Computer Science & IT

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ANALISIS KEPUASAN LAYANAN BIRO ADMINISTRASI AKADEMIK KEMAHASISWAAN STMIK WIDURI MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER Kharisma, Nanda; Pusparini, Nur Nawaningtyas
JURNAL ILMIAH INFORMATIKA Vol 13 No 01 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i01.9427

Abstract

Higher education institutions play an important role in creating quality human resources, and one of the main factors that influence the quality of higher education is the academic services provided to students, as seen at STMIK Widuri. The Academic and Student Affairs Administration Bureau of STMIK Widuri needs to provide quick and accurate responses to ensure the quality of services is maintained. This study aims to predict the services of the Academic and Student Affairs Administration Bureau using the Naive Bayes Classifier algorithm, which is a probability-based classification method for predicting a class. The datasets used in this study are perceptions and expectations derived from questionnaires distributed online to STMIK Widuri students, which were processed using RapidMiner through the stages of Knowledge Discovery in Database (KDD). The evaluation results show an accuracy of 95%, precision of 100%, and recall of 93.75% for the perception dataset, and an accuracy of 90%, precision of 87.50%, and recall of 87.50% for the expectation dataset. This algorithm has proven to be effective in predicting the satisfaction of services provided by the Academic and Student Affairs Administration Bureau at STMIK Widuri.
ANALISIS TINGKAT KEPUASAN PELANGGAN TERHADAP PELAYANAN CONTACT CENTER PLN 123 MENGGUNAKAN METODE PIECES Shosa, Andi; Pusparini, Nur Nawaningtyas; Kharisma, Nanda; Samuel, Samuel
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10125

Abstract

As the demands for quality service in the digital era increase, strategic companies such as PT. PLN (Persero) utilize Contact Center 123 as the front line of communication with customers. The background of the success of other digital innovations such as the PLN Mobile application which has been proven to significantly increase customer satisfaction, emphasizes the urgency of continuous evaluation of all service channels. This study aims to measure the level of customer satisfaction with the PLN 123 Contact Center service and provide strategic recommendations for improving quality in the future. The method used is a comprehensive analysis with the PIECES framework, which evaluates six dimensions: Performance, Information, Economy, Control, Efficiency, and Service. The results of the study showed very satisfactory system performance, with all dimensions achieving a level of conformity above 90%. The Control aspect obtained the highest score (94.81%), while the Efficiency aspect was recorded as the lowest (90.26%). Although the overall performance was very good, these findings indicate that there is clear room for improvement in the efficiency area to improve the customer experience.
PREDIKSI MAHASISWA INSTITUT SOSIAL DAN TEKNOLOGI WIDURI JAKARTA BERPOTENSI DROP OUT MENGGUNAKAN ALGORITMA NAÏVE BAYES Sultan, Sultan; Pusparini, Nur Nawaningtyas; Kharisma, Nanda; Samuel, Samuel
JURNAL ILMIAH INFORMATIKA Vol 13 No 02 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i02.10310

Abstract

Universities are responsible for producing quality graduates and reducing dropout rates (DO), a serious challenge for the Widuri Institute of Social and Technology (ISTEK). This phenomenon has a negative impact on the quality of education and accreditation, making early identification of students who have the potential to drop out (DO) very crucial. This study aims to apply the Naïve Bayes algorithm to predict the potential for dropout (DO) of ISTEK Widuri students based on data on the activities of the 2021, 2022, and 2023 intakes. Naïve Bayes has proven effective in classifying students at risk of dropping out (DO). The Semester Credit Unit (SKS) attribute is the most dominant indicator, students with low SKS have a high potential for dropping out (DO). Model performance varies for each batch, in the 2021 batch it reached 90% accuracy (100% DO precision, 40% recall), the 2022 batch showed 93.75% accuracy (100% DO precision, 60% DO recall), and the 2023 batch had 86.67% accuracy (100% DO precision, 33.33% DO recall). This model is very good at validating students who are safe from DO (100% recall of Not DO in all batches). Even so, the model still needs to be improved so that it can find all students who are at risk of dropping out (DO) as a whole. The prediction results for students with the potential for DO at ISTEK Widuri Jakarta are expected to support more optimal prevention efforts and contribute to improving the quality of education.
Co-Authors Pusparini, Nur Nawaningtyas Samuel Samuel Shosa, Andi Sultan Sultan