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All Journal Semantik JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi JURTEKSI Jurnal Teknologi Komputer dan Sistem Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Bantenese Jurnal Pengabdian Masyarakat Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) TIN: TERAPAN INFORMATIKA NUSANTARA TEPIAN Jurnal Sistem Informasi dan Sistem Komputer JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Bulletin of Information Technology (BIT) J-Intech (Journal of Information and Technology) DiJITAC : Digital Journal of Information Technology and Communication Informatika Wicida Ensiklopedia Research and Community Service Review Journal of Artificial Intelligence and Digital Business JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Amelia Yusnita
STMIK Widya Cipta Dharma
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Analisis Kepuasan Pegawai Terhadap Aplikasi E-KGB Pada Badan Kepegawaian Daerah Menggunakan Metode End User Computing Satisfaction Paul, Debora; Yusnita, Amelia; Pahrudin, Pajar
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.456

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kepuasan pegawai terhadap aplikasi e-KGB (Elektronik Kenaikan Gaji Berkala) pada Badan Kepegawaian Daerah (BKD) Provinsi Kalimantan Timur Menggunakan Metode End User Computing Satisfaction (EUCS). Aplikasi e-KGB dirancang untuk mempermudah proses administrasi kenaikan gaji berkala bagi pegawai di BKD Kaltim. Namun, penerimaan dan kepuasan pegawai terhadap aplikasi ini masih perlu dievaluasi untuk memastikan keberhasilan implementasi dan memberikan dasar bagi perbaikan aplikasi di masa depan. Data diperoleh melalui penyebaran kuesioner kepada pegawai yang menggunakan aplikasi e-KGB di BKD Provinsi Kalimantan Timur. Responden diminta untuk memberikan penilaian terhadap masing-masing dimensi EUCS berdasarkan pengalaman mereka dalam menggunakan aplikasi tersebut. yang mengukur kepuasan pengguna berdasarkan lima dimensi EUCS, yaitu Content, Accuracy, Format, Ease of Use dan Timeliness. Hasil penelitian menunjukkan bahwa sebagian besar pegawai merasa puas dengan aplikasi e-KGB, terutama terkait kemudahan penggunaan dan kualitas informasi yang diberikan.
Analisis Pola Penjualan Obat di Apotek Menggunakan Algoritma Apriori Untuk Optimalisasi Stok dan Penjualan Yulindawati, Yulindawati; Yusnita, Amelia; Mayasari, Renni; Melano, M Erick
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5407

Abstract

This research aims to identify product sales patterns at Teluk Bayur Pharmacy to optimize stock management and increase sales by using data mining techniques, especially the Apriori Algorithm. Pharmacies are very instrumental in providing drug-related information and are a form of retail trade that sells medicines at more affordable prices compared to hospital services. However, Teluk Bayur Pharmacy often faces difficulties in managing stock, analyzing product sales patterns and consumer behavior, which causes problems of over stock or under stock. Through the application of Association Rule Mining using the Apriori Algorithm, this research analyzes the correlation between products to find frequent purchase patterns. The methods used include literature study, data collection, data preprocessing, application of Apriori Algorithm, evaluation and interpretation of results, and application of conclusions and recommendations. To analyze sales patterns, the data collected exceeded 100 entries, and 12 transactions were selected that represented the most sales each month. The results of testing the analysis utilizing tanagra 1.4.41 software, by setting a minimum support of 40% and a minimum confidence of 70%, from the results of research and testing show that products that are often purchased together by customers are masks, vegeta, and antimo with a confidence value above 70%. The findings are expected to provide insight for Teluk Bayur Pharmacy in understanding consumer behavior and identifying new sales opportunities.
Clustering the Economic Conditions of Various Countries From 2010-2023 by Conducting A Comparative Analysis of the K-Means and K-Medoids Algorithms Yunita, Yunita; Ekawati, Hanifah; Yusnita, Amelia
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7330

Abstract

Understanding the similarities and differences in economic conditions across countries is crucial for various stakeholders. This research investigates the global economic landscape by clustering countries based on their economic indicators, including GDP, inflation rate, unemployment rate, and economic growth, spanning the period of 2010 to 2023. This timeframe encompasses significant global economic events, making it pertinent for analysis. The study employs and compares two prominent clustering algorithms: K-Means and K-Medoids, to identify groups of countries exhibiting similar economic patterns. Utilizing secondary data from Kaggle encompassing 19 countries, the research assesses the ability of each algorithm to delineate meaningful economic clusters. The K-Means algorithm, with a determined optimal number of four clusters, demonstrated a reasonably good cluster separation and moderate internal cohesion, evidenced by a Silhouette Coefficient of 0.58 and a Davies-Bouldin Index of 0.63. In contrast, the K-Medoids algorithm yielded a distinct clustering structure with a lower Silhouette Coefficient (0.26) and a higher Davies-Bouldin Index (1.16), suggesting less distinct cluster separation and potential sensitivity to data characteristics. This comparative analysis provides insights into the applicability and performance of K-Means and K-Medoids in discerning global economic structures, contributing to a deeper understanding of the world economic map and the utility of clustering techniques in economic data analysis.
REKOMENDASI PEMILIHAN JUDUL TUGAS AKHIR MENGGUNAKAN METODE NAÏVE BAYES Yulindawati, Yulindawati; Lailiyah, Siti; Yusnita, Amelia; Hafifah, A.
Journal of Information System Management (JOISM) Vol. 5 No. 2 (2024): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2024v5i2.1383

Abstract

Syarat untuk menjadi sarjana adalah telah menyelesaikan tugas akhir dengan membuat suatu artikel ilmiah dalam bentuk buku atau yang disebut skripsi. Dalam mencari referensi disarankan oleh program studi khusunya program studi teknik informatika adalah sesuai dengan kemampuan dan minat mahasiswa. Vaktor yang menyebabkan mahasiswa tidak lulus tepat waktu adalah mahasiswa kesulitan dalam menentukan topik tugas akhir yang disebabkan oleh kurangnya pengetahuan dari matakuliah pendukung. Berdasarkan hal tersebut dibuatlah sistem yang bersifat rekomendasi pemilihan topik tugas akhir. Sistem rekomendasi menggunakan algoritma naïve bayes dan menggunakan tahapan data mining. metode pengembangannya adalah waterfall, penelitian mengunakan data mahasiswa program studi teknik informatika angkatan 2014-2018, sebanyak 240 data yang digunakan sebagai perhitungan, dari hasil pengelompokan berdasarkan buku kurikulum program studi teknik informatika didapat 7 atribut mata kuliah untuk dijadikan sebagai variabel. Pada tahap desain, alur sistem digambarkan dengan menggunakan flowchart. Hasil implementasinya dalam bentuk aplikasi dimana mahasiswa dapat melakukan penginputan nilai matakuliah yang telah ditempuh, proses perhitungan probabilitas akan menampilkan nilai tertinggi yang akan digunakan sebagai rekomendasi topik tugas akhir.
Penerapan Algoritma Support Vector Machine (SVM) dalam Analisis Sentimen Mahasiswa Terhadap Sistem Layanan KPST STMIK Widya Cipta Dharma Maulana Umar; Pitrasacha Adytia; Amelia Yusnita
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9189

Abstract

Abstrak - Sistem Layanan KPST di STMIK Widya Cipta Dharma menghadapi tantangan disparitas kepuasan mahasiswa, terutama dalam aspek administratif dan bimbingan akademik. Tujuan: Penelitian ini bertujuan untuk menganalisis sentimen mahasiswa secara komprehensif menggunakan algoritma Support Vector Machine (SVM) dengan dataset aktual, mengoptimasi hyperparameter, dan mengatasi ketidakseimbangan data. Metode: Data dikumpulkan melalui survei terstruktur (N=150 responden), diproses dengan TF-IDF (ngram_range=(1,3), max_features=1500), dan diklasifikasi menggunakan SVM dengan teknik GridSearchCV untuk optimasi parameter (C=10, gamma=0.01, kernel=RBF). SMOTE diterapkan untuk menangani ketidakseimbangan kelas. Hasil: Model mencapai akurasi 82.3% dengan presisi 80.1% pada kelas minoritas (negatif). Analisis mengungkap sentimen positif dominan (68%) pada kecepatan respons admin (skor 4.0), namun isu kritis teridentifikasi di antarmuka pengguna (32% komentar negatif) dan kualitas bimbingan (skor 3.5). Kesimpulan: Penelitian ini membuktikan efektivitas SVM dalam analisis sentimen akademik, dengan rekomendasi spesifik untuk redesain UI/UX dan integrasi sistem notifikasi otomatis. Temuan juga menyoroti pentingnya penanganan ketidakseimbangan data dalam klasifikasi teks.Kata kunci: Analisis Sentimen; Support Vector Machine (SVM); Sistem Layanan KPST; Ketidakseimbangan Data; Optimasi Hyperparameter; UI/UX Akademik.                            Abstract - The KPST Service System at STMIK Widya Cipta Dharma faces challenges in student satisfaction disparities, particularly in administrative and academic guidance aspects. Objective: This study aims to comprehensively analyze student sentiment using the Support Vector Machine (SVM) algorithm with real-world datasets, optimizing hyperparameters and addressing data imbalance. Method: Data was collected through structured surveys (N=150 respondents), processed with TF-IDF (ngram_range=(1,3), max_features=1500), and classified using SVM with GridSearchCV for parameter optimization (C=10, gamma=0.01, kernel=RBF). SMOTE was applied to handle class imbalance. Results: The model achieved 82.3% accuracy with 80.1% precision for the minority class (negative). Analysis revealed dominant positive sentiment (68%) on administrative response (score 4.0), but critical issues were identified in user interface (32% negative feedback) and academic guidance quality (score 3.5). Conclusion: This research demonstrates SVM's effectiveness in academic sentiment analysis, with specific recommendations for UI/UX redesign and automated notification system integration. Findings also highlight the importance of addressing data imbalance in text classification.Keywords: Sentiment Analysis; Support Vector Machine (SVM); KPST Service System; Data Imbalance; Hyperparameter Tuning; Academic UI/UX.
Development of the Mr. Klin Laundry Website Using the Waterfall Method for Service Optimization Yonatan, Kevin; Yusnita, Amelia; Azahari, Azahari
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2708

Abstract

This study aims to develop the Mr Klin Laundry website as a digital solution to improve the effectiveness and quality of services provided to customers. The main problem faced by this business unit is the limitation of ordering processes and service information, which are still carried out manually, thereby hindering service speed, data accuracy, and customer satisfaction. The Waterfall method is used in system development because it consists of structured stages, including requirements analysis, design, implementation, testing, and maintenance. The results of the study show that the developed website is capable of providing online service ordering features, laundry status tracking, pricing information, and customer communication. System testing using black box testing indicates that all functions operate in accordance with the specified requirements. With the presence of this website, service processes become faster, more transparent, and more easily accessible to customers, thereby supporting service optimization and enhancing the competitiveness of Mr Klin Laundry.
Penerapan Algoritma XGBoost Dalam Prediksi Harga Sewa Kos Di Kota Samarinda Rahman, Amalia; Yusnita, Amelia; Ekawati, Hanifah
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2304

Abstract

The population growth and increasing economic activity in Samarinda City have led to a rising demand for temporary housing such as boarding houses. However, rental price determination is still largely based on the owner’s intuition rather than objective factors such as available facilities, room specifications, transportation accessibility, and proximity to public amenities. This study aims to develop a rental price prediction model for boarding houses using the Extreme Gradient Boosting (XGBoost) algorithm with a Knowledge Discovery in Database (KDD) approach. The research data were collected through a web scraping process from the Mamikos platform, yielding 231 initial records, which were then cleaned and filtered for outliers, resulting in 225 valid data points. Five main features derived from feature engineering were utilized in the model, namely Facility Score, Combined Specification Score, Nearest Place Score, Transportation Score, and Rental System Score. The evaluation results show that the XGBoost model achieved a Mean Absolute Error (MAE) of Rp348,822, a Root Mean Squared Error (RMSE) of Rp416,139, and a coefficient of determination (R²) of 0.612. These values indicate that the model can explain 61.2% of the variation in rental prices with reasonably good predictive performance. The feature importance analysis reveals that Facility Score and Combined Specification Score are the most influential factors affecting rental prices, while transportation and rental system factors contribute less significantly. This study is expected to serve as a reference for boarding house owners, tenants, and policymakers in determining more objective and competitive rental prices based on a data mining approach.
Classification of Diabetes Diseases Based on Medical Features Using Optimized Support Vector Machine Arfyanti, Ita; Yusnita, Amelia; Adytia, Pitrasacha
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8880

Abstract

Diabetes mellitus is a chronic disease caused by impaired glucose metabolism and has become a global health threat with a steadily increasing prevalence each year. According to WHO and IDF, the number of people living with diabetes is projected to reach 783 million by 2045. This condition demands the development of an accurate and efficient early detection system to support medical decision-making. This study aims to develop an optimized Support Vector Machine (SVM)-based classification model to enhance the accuracy and interpretability of diabetes prediction. The dataset used is the Pima Indians Diabetes Dataset, which consists of eight medical features such as glucose level, blood pressure, and body mass index (BMI). The research stages include data preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), parameter optimization with GridSearchCV, and interpretability analysis through SHapley Additive exPlanations (SHAP). The results show that the optimized SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 82%, with a significant improvement in the diabetes class recall value from 0.564 to 0.83 after optimization. The Area Under Curve (AUC) value of 0.871 indicates the model’s effectiveness in distinguishing between positive and negative classes. The SHAP analysis reveals that Glucose, Age, BMI, and Diabetes Pedigree Function are the most influential features in prediction. These findings emphasize that the combination of normalization, balancing, hyperparameter optimization, and interpretability produces a reliable and transparent SVM model. This model has strong potential for implementation in Clinical Decision Support Systems (CDSS) for accurate and explainable early diabetes detection.
ANALYSIS OF INTEREST IN USING BLU DEPOSIT BASED ON TAM Pangestu, Nathania Clarissa; Pratiwi , Heny; Yusnita, Amelia
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4239

Abstract

Abstract: Digital banking has brought various innovations in financial services, one of which is Blu Deposito by BCA Digital. However, the adoption rate of digital deposit services is still relatively low compared to digital payment services. This study aims to identify and analyze the factors that influence customers' intentions and actual behavior in using Blu Deposito with reference to the Technology Acceptance Model (TAM). This study aims to analyze the factors that influence customers' intentions and actual behavior in adopting Blu Deposito using the Technology Acceptance Model (TAM) framework. Data was collected through a Google Form questionnaire from 54 customers at one BCA branch and analyzed using SPSS through validity and reliability tests, descriptive analysis, and multiple regression. The results show that Behavioral Intention (BI)is significantly influenced by Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Attitude Toward Using (ATU), with PEOU as the most dominant factor. In addition, BI has a significant effect on Actual System Use (AU), which confirms the relevance of applying the TAM model in the context of digital deposit products. These findings indicate that ease of use plays a greater role than financial benefits in encouraging users to adopt Blu Deposits. This study contributes to the understanding of digital deposit adoption and provides managerial insights to improve the usability and user engagement of digital banking services. Keywords: actual system use; attitude toward using; behavioral intention; perceived ease of use; perceived usefulness; technology acceptance model Abstrak: Perbankan digital telah menghadirkan berbagai inovasi dalam layanan keuangan, salah satunya Blu Deposito oleh BCA Digital. Meskipun demikian, tingkat adopsi terhadap layanan deposito digital masih relatif rendah dibandingkan dengan layanan pembayaran digital. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis faktor-faktor yang memengaruhi niat serta perilaku aktual nasabah dalam menggunakan Blu Deposito dengan mengacu pada kerangka Technology Acceptance Model (TAM). Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi niat dan perilaku aktual nasabah dalam mengadopsi Blu Deposito dengan menggunakan kerangka Technology Acceptance Model (TAM). Data dikumpulkan melalui kuesioner Google Form dari 54 nasabah di satu cabang BCA dan dianalisis menggunakan SPSS melalui uji validitas, reliabilitas, analisis deskriptif, dan regresi berganda. Hasil penelitian menunjukkan bahwa Behavioral Intention (BI) dipengaruhi secara signifikan oleh Perceived Ease of Use (PEOU), Perceived Usefulness (PU), dan Attitude Toward Using (ATU), dengan PEOU sebagai faktor paling dominan. Selain itu, BI berpengaruh signifikan terhadap Actual System Use (AU), yang menegaskan relevansi penerapan model TAM pada konteks produk deposito digital. Temuan ini menunjukkan bahwa kemudahan penggunaan memiliki peran lebih besar dibandingkan manfaat finansial dalam mendorong pengguna untuk mengadopsi Blu Deposito. Penelitian ini berkontribusi terhadap pemahaman adopsi deposito digital serta memberikan wawasan manajerial untuk meningkatkan kegunaan dan keterlibatan pengguna pada layanan perbankan digital. Kata kunci: actual system use; attitude toward using; behavioral intention; perceived ease of use; perceived usefulness; technology acceptance model
Co-Authors Adrian, Nurul Ahmad Rofiq Hakim Alenya, Nia Andi Yushika Rangan Andi Yusika Rangan Astuty Nilasari Azahari, Azahari Basrie Dewi Rosita Ekawati Yulsilviana Ekawati, Hanifah Ekawati, Hanifah Elys Diawati Emon Azriadi Eri Wandana Fachry Abda El Rahman Fadri, Roy Febryanto, Febryanto Fery Setiawan Hafifah, A. Harianto, Kusno Haristyawan, Ivan Hariyadi Hariyadi Heny Pratiwi I Gede Adi Merta Ita Arfyanti Khoiri Saumahudi Kusnandar Kusnandar Kusnandar Lukman Hariri M. Irwan Ukkas Irwan Ukkas Ukkas Maulana Umar Mayasari, Reny Melano, M Erick Muhammad Awaludin Muhammad Ridhuan. S Nila Ratna Wati Pahrudin, Pajar Pajar Pahruddin Pangestu, Nathania Clarissa Patrianto, Wahyu Paul, Debora Pitrasacha Adytia Pratiwi , Heny Putri Wulandari Rahman, Amalia Raya, Dimas Renni Mayasari Roba, Al-Muchlis Syachrul Ramadani Rosiana Handini Salmon Salmon Siti Lailiyah Siti Qomariah Siti Qomariah Susanti, Agustina Nona Tabrani Rija’i Theana Dwi Aprillita Vilianty Rafida Yonatan, Kevin Yulindawati, Yulindawati Yulindawati, Yulindawati Yunita Yunita