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All Journal CogITo Smart Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar Advances in Human Resource Management Research
Chrisanti, Ibrena Reghuella
Universitas Klabat
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Other

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Analisis Kepuasan Pelanggan Terhadap Pelayanan Mobile Banking PT. Bank XYZ Wilayah Airmadidi Menggunakan E-Servqual Andrew Tanny Liem; Ibrena Reghuella Chrisanti; Alvin Sandag; Dipta Divakara Pius Purwadaria
CogITo Smart Journal Vol 6, No 2 (2020): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v6i2.277.229-238

Abstract

Pesatnya perkembangan teknologi smartphone di Indonesia mendorong pelaku bisnis untuk memanfaatkanya sebagai sarana untuk mengembangkan bisnis, baik itu berupa layanan ataupun produk sehingga dapat memberikan layanan yang terbaik kepada pelanggan. Bank XYZ adalah salah satu badan usaha milik negara Indonesia yang bergerak dibidang perbankan yang menyediakan layanan perbankan online yaitu Mobile Banking untuk memudahkan nasabah melakukan transaksi. Penelitian ini bertujuan untuk mengetahui seberapa besar tingkat kepuasan nasabah pengguna Mobile Banking khususnya pada Bank XYZ Wilayah Airmadidi. Dengan menggunakan metode e-Servqual yang merupakan suatu teknik evaluasi untuk menilai persepsi pelanggan terhadap kualitas layanan elektronik dengan tujuh variabel didalamnya. Hasil analisis linear regresi berganda menunjukkan bahwa e-Servqual berpengaruh sebesar 64.4% terhadap kepuasan nasabah pengguna Mobile Banking dan 34.6% dipengaruhi oleh faktor lain yang tidak diteliti pada penelitian ini. Pengujian hipotesis secara parsial terdapat tiga variabel yaitu fulfillment, privacy, dan contact memiliki pengaruh terhadap kepuasan nasabah dan empat variabel lain yaitu efficiency, reliability, responsiveness, dan compensation tidak memiliki pengaruh terhadap kepuasan nasabah pengguna Mobile Banking Bank XYZ. Pengujian secara simultan e-Servqual memiliki pengaruh yang terhadap kepuasan nasabah pengguna Mobile Banking bank XYZ.
Sentiment Analysis and Topic Detection on Post-Pandemic Healthcare Challenges: A Comparative Study of Twitter Data in the US and Indonesia Tangka, George Morris William; Chrisanti, Ibrena Reghuella; Waworundeng, Jacquline; Maringka, Raissa Camilla; Sandag, Green Arther
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.819.561-579

Abstract

This study examines public sentiment and key topics in Twitter discussions regarding the COVID-19 vaccine and the Omicron variant in the US and Indonesia. The importance of this research lies in understanding people's changing views on vaccination, especially in light of new virus variants. Using sentiment analysis with VADER and topic modeling with Latent Dirichlet Allocation (LDA), this research analyzes 637,367 tweets from the US and 91,679 tweets from Indonesia collected over two months from January 21 to February 21, 2022. The results reveal that US discussions on vaccines are predominantly positive, while those on Omicron are mostly negative. In contrast, discussions in Indonesia are largely neutral, followed by positive sentiment. Additionally, five main topics were identified for each country, with the US showing a broader range of vaccine-related discussions. These findings suggest that while the vaccine is seen as a source of hope in both countries, factors such as literacy, socioeconomic status, and education contribute to negative sentiment and vaccine resistance.
Integrasi MEREC dan MOORA untuk Pemilihan Ketua OSIS Oktoverano Lengkong; Edson Yahuda Putra; Ibrena Reghuella Chrisanti
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6922

Abstract

The selection of a student council president requires an objective assessment process because the decision affects student leadership and the continuity of student programs. At SMK Yadika Manado, the assessment of student council president candidates may still be influenced by subjectivity and requires relatively long processing time when performed manually. This study develops a web-based decision support system for selecting the student council president using a combination of MEREC and MOORA. MEREC is used to determine objective criteria weights based on the removal effect of each criterion, while MOORA is used to rank alternatives using normalized values and criteria weights. The test data consist of three candidates evaluated using four criteria: leadership, responsibility, discipline, and achievement. The results show that responsibility has the highest weight of 0.262295, followed by discipline at 0.252831, achievement at 0.248925, and leadership at 0.235948. The final ranking identifies Keanu Mangundap as the best alternative with a score of 0.672946. The developed system supports candidate data management, criteria management, weight calculation, and transparent ranking presentation.
Comparative Analysis of Machine Learning Models for Emotion Prediction in Reviews of Large Language Model-Powered Applications Semmy Wellem Taju; George Morris William Tangka; Ibrena Reghuella Chrisanti
Advances in Human Resource Management Research Vol. 4 No. 3 (2026): June - September
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/ahrmr.v4i3.1008

Abstract

Purpose: This study compares the performance of conventional machine learning algorithms for multiclass emotion prediction in reviews of Large Language Model (LLM)-powered applications using a unified TF-IDF representation. Research Method: User reviews were collected from the Google Play Store for eight popular LLM applications through web scraping. After text preprocessing, reviews were labeled into five emotion categories (Happy, Sadness, Anger, Fear, and Surprise). Ten machine learning algorithms, namely Perceptron, KNN, Naïve Bayes, Logistic Regression, MLP, SVM (Linear and RBF), Random Forest, XGBoost, and LightGBM, were evaluated using Accuracy, Balanced Accuracy, Precision, F1-score, MCC, and ROC-AUC. Result and Discussion: XGBoost achieved the best performance, with 87.30% Accuracy, 77.84% Balanced Accuracy, 64.28% F1-score, and 0.5626 MCC. ROC-AUC values between 0.91 and 0.96 demonstrate excellent discrimination across all emotion classes. The superior performance of XGBoost indicates that gradient boosting effectively captures informative patterns from sparse TF-IDF features. Implication: The findings provide practical guidance for selecting efficient machine learning models for emotion analysis of LLM application reviews and support AI application improvement through user emotion understanding. Originality: This study presents a comprehensive benchmark of ten conventional machine learning algorithms using a large-scale multi-application LLM review dataset and a unified five-emotion classification framework.
IMPLEMENTASI BWM–MOORA DALAM MODEL KEPUTUSAN SELEKSI PENERIMA BEASISWA BERPRESTASI Edson Yahuda Putra; Ibrena Reghuella Chrisanti; Ambalao, Shapely Smart
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 03 (2026): Volume 11 No. 03, September 2026 Processed
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i03.62437

Abstract

Scholarship selection in higher education requires a transparent procedure because candidates are evaluated through criteria with different levels of importance. This study develops a decision support model that integrates the Best–Worst Method (BWM) for criterion weighting and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) for candidate ranking. The data comprise nine anonymized scholarship candidates evaluated using academic achievement, cumulative grade point average (GPA), and administrative completeness. In the BWM model, GPA was designated as the best criterion and administrative completeness as the worst criterion. The resulting weights were 0.2308 for academic achievement, 0.6923 for GPA, and 0.0769 for administrative completeness, with a fully consistent comparison structure. MOORA calculations were performed using the candidates’ original GPA values rather than converting them into ordinal categories. The results placed alternative A5 first with an optimization value of 0.3509, followed by A2 with 0.3464 and A6 with 0.3426. Because all candidates satisfied the administrative requirement, this criterion contributed the same value to every alternative and did not change the ranking. Sensitivity analysis showed that the top three alternatives remained stable under moderate changes in the relative weights of achievement and GPA, but the ranking changed when GPA became strongly dominant. The model provides a reproducible basis for scholarship decisions while highlighting the need to use administrative completeness as an eligibility filter and to expand substantive assessment criteria in future implementation.
Co-Authors Alvin Sandag Andrew Tanny Liem Dipta Divakara Pius Purwadaria Edson Yahuda Putra George Morris William Tangka George Morris William Tangka Green Arther Sandag Jacquline Waworundeng Maringka, Raissa Oktoverano Lengkong Semmy Wellem Taju Shapely Ambalao