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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Komputer AMIK BSI Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa Techno Nusa Mandiri : Journal of Computing and Information Technology JTIK (Jurnal Teknik Informatika Kaputama) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Bianglala Informatika Jurnal Infortech
Erma Delima Sikumbang
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Other

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Model ITPOSMO untuk Evaluasi Keberhasilan Aplikasi Bravo Awaludin, Rizky; Ramanda, Kresna; Puspitasari, Diah; Sikumbang, Erma Delima
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.754

Abstract

Bravo-PUPR application problems are caused by many factors, inaccurate data, inadequate integration, technical infrastructure and high process complexity. In addition, inconsistencies between organizational values and digital innovation, lack of training, weak coordination, and external support are also obstacles to the success of such e-government. To find the success rate of the Bravo-PUPR application, we used the ITPOSMO model to assess the gaps between the designs realized in the application. Of the 39,308 employees in the population used for the study, only 100 met the criteria to be selected using the Purpose Sampling technique. Quantitative data analysis is used, primarily to test for gaps. As a result, the Other Resource dimension received the lowest GAP score, which was 0.09. In addition, Staffing and Skills dimension received a score of 0.76, Objective and value obtained a score of 1.17, Technology obtained a score of 1.61, Information obtained a score of 1.69, and Management and Structure obtained a score of 1.82. The highest GAP score in the Process dimension is 2.44. This score is obtained based on the calculation of the overall assessment table. The project may have been successful due to its overall rating of 9.58, which ranges from 0 to 14.
Pendekatan Algoritma Naive Bayes Dalam Memprediksi Penyakit Diabetes Qudsiah Azizah; Diah Puspitasari; Sulaeman Hadi Sukmana; Erma Delima Sikumbang; Kresna Ramanda
Jurnal Infortech Vol. 7 No. 2 (2025): Desember 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v7i2.11397

Abstract

Diabetes melitus merupakan penyakit metabolik yang bersifat kronis dan multifaktorial. Penyakit ini menunjukkan gejala peningkatan kadar gula darah (hiperglikemia) akibat proses metabolisme karbohidrat yang tidak normal, lemak, dan protein yang tidak normal. Hingga saat ini, terdapat lebih dari 150 juta orang yang tercatat di seluruh dunia mengidap penyakit ini dan perkembangan penyakit yang terus meningkat dapat menyebabkan komplikasi yang fatal. Faktanya, sebagian besar masyarakat mengabaikan tanda-tanda awal ini: rasa lapar yang berlebihan, rasa lelah yang tidak wajar, dan luka yang lambat sembuh. Penelitian ini dilakukan untuk analisis algoritma Naïve Bayes pada klasifikasi penyakit diabetes untuk mendapatkan hasil optimal dengan akurasi yang ditawarkan dengan cara ini. Dataset diambil dari website Kaggle yang berjumlah 10.000 data dengan jumlah 2 kelas yaitu Diabetes dan Non Diabetes, kelas Diabetes mencakup sebanyak 8.500 data, sementara kelas Non-Diabetes mencakup 91.500 data. Metode yang digunakan yaitu Algoritma Naïve Bayes. Hasil pengujian menunjukkan bahwa Naïve Bayes mencapai tingkat akurasi sebesar 90,66% yang artinya Algoritma Naive Bayes adalah metode yang baik dan tepat untuk mengklasifikasikan penyakit diabetes.
Penerapan Algoritma K-Nearest Neighbors (KNN) Dalam Analisis Sentimen Ulasan Pengguna Aplikasi JMO (Jamsostek Mobile) Ghifari Fatihah Rabbani; Kresna Ramanda; Sulaeman Hadi Sukmana; Qudsiah Nur Azizah; Erma Delima Sikumbang
Bianglala Informatika Vol. 14 No. 1 (2026): Maret 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v14i1.11335

Abstract

JMO (Jamsostek Mobile) is an official application launched by BPJS Ketenagakerjaan, designed to support workers in facilitating access to social protection. However, several obstacles are experienced by application users, such as errors in the application, difficulties logging in, and JHT balances not being displayed. To obtain a general overview of JMO application user sentiment, an evaluation is needed to capture user concerns. Therefore, this study will explore the precision of the KNN algorithm in analyzing sentiment from JMO application user reviews. The objectives of this research are to identify and analyze user feedback, classify overall sentiments, implement sentiment analysis, and test the accuracy of the KNN algorithm. This study adopts a quantitative approach, applying numerical data analysis through text mining techniques. From a dataset of 10,000 reviews collected via web scraping and refined Preprocessing, 7,185 reviews were obtained, revealing that 51.38% expressed positive sentiment. The KNN algorithm achieved its highest accuracy 76.2%, precision 76.2%, recall 78.0%, and F1-score 77.1% at K = 21 under 90%-10% data split. Furthermore, the model’s AUC score of 0.7617 indicated fair and reasonably good performance. These findings suggest that the KNN classification model is capable of providing balanced classification results across classes, leading to a fairer and less biased evaluation.
Breast Cancer Prediction Optimization Using Support Vector Machine and Naive Bayes Algorithms Devi Wulandari; Qudsiah Azizah; Diah Puspitasari; Kresna Ramanda; Erma Delima Sikumbang; Sulaeman Hadi Sukmana
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.12629

Abstract

Breast cancer is ranked as the second most common cause of death for women worldwide. Breast cancer is often found when it has entered the final stage. In general, this is due to slow handling and treatment, so it is very necessary to detect the disease early. The purpose of this study is to determine the performance of the two algorithms, namely Naïve Bayes and Support Vector Machine (SVM) in classifying breast cancer types which will then be analysed and compared the accuracy of the two algorithms. The dataset used in this study, Breast Cancer Wisconsin, is public data originating from UCI Machine Learning, has a total of 683 data with 10 attributes and has two classes, namely benign class with 458 data and malignant class with 241 data. The dataset was split 80:20, with 80% used as training data and 20% as testing data, and then evaluated using cross-validation. The results of the study show that Support Vector Machine (SVM) has the best performance with an accuracy of 96.89% while Naïve Bayes 96.15%. With this accuracy, These results indicate that the SVM model provides better classification performance than Naïve Bayes for the Breast Cancer Wisconsin dataset.
SISTEM PENDUKUNG KEPUTUSAN PENGADAAN OBAT BERBASIS WEB MENGGUNAKAN ALGORITMA APRIORI PADA TOKO OBAT LARASATI FARMA Risdiani; Erma Delima Sikumbang; Eko Yulianto
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 2 (2026): Artificial Intelligence (AI)
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i2.1392

Abstract

Pengelolaan pengadaan obat yang masih dilakukan secara manual dapat menyebabkan ketidakseimbangan persediaan, seperti terjadinya kekosongan stok pada obat yang banyak diminati maupun pennumpukan obat yang berisiko kedaluwarsa. Tujuan dari penelitian ini adalah untuk menciptakan sistem pendukung keputusan berbasis web yang dapat memberikan saran pengadaan obat berdasarkan pola pembelian konsumen dengan memanfaatkan algoritma Apriori. Penelitian ini dilaksanakan dengan metode Research and Development yang mencakup identifikasi kebutuhan, pengumpulan data melalui observasi, wawancara, serta studi pustaka, kemudian dilanjutkan dengan perancangan, implementasi, dan pengujian sistem. Algoritma Apriori digunakan pada lebih dari 500 data transaksi penjualan Toko Obat Larasati Farma untuk membangun frequent itemset dan aturan asosiasi dengan parameter nilai minimum support 10% dan minimum confidence 40%. Sistem ini dikembangkan menggunakan Vue.js sebagai frontend, Spring Boot sebagai backend, dan MySQL sebagai database. Hasil penelitian menunjukkan bahwa sistem berhasil menghasilkan aturan asosiasi yang merepresentasikan pola pembelian obat sehingga dapat digunakan sebagai dasar rekomendasi pengadaan. Implementasi sistem membantu proses analisis data transaksi menjadi lebih terstruktur dan mendukung pengambilan keputusan pengadaan obat secara lebih efektif dibandingkan metode konvensional. Kata Kunci : Algoritma Apriori, Aturan Asosiasi, Market Basket Analysis, Pengadaan Obat, Sistem Pendukung Keputusan
Co-Authors Adi Supriyatna Agustina, Bella Anggun Setiadi Arief Rusman Arsyan Syaina Ahmad Awaludin, Rizky Azizah, Qudsiah Nur Deri Saputra, Deri Devi Wulandari Diah Puspitasari EKO YULIANTO Fattya Ariani Ghifari Fatihah Rabbani Hadi Sukmana, Sulaeman Handayani, Tiwi Illahiya, Elfa Kresna Ramanda Lia Mazia, Lia Mely Mailasari Muhammad Rifai Muhammad, Indra Maulana Prima, Harry Pujiati, Tri Wahyuni Qudsiah Azizah Qudsiah Nur Azizah Risdiani Rizqi Agung Permana Sucitra Sahara Sundari, Wiwin Syifa Fauziah Wida Prima Mustika