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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan Guru (JPG) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research JUTECH : Journal Education and Technology Malcom: Indonesian Journal of Machine Learning and Computer Science Indonesian Science Education Journal (ISEJ) INOVTEK Polbeng - Seri Informatika
Oktavia, Lola
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Penilaian Penerimaan E-Government Di Indonesia Oktavia, Lola
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 6, No 1 (2020): Juni 2020
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.599 KB) | DOI: 10.24014/coreit.v6i1.9143

Abstract

Penerapan E-Government di Indonesia masih sebatas instansi tertentu. Berdasarkan hasil survey oleh United Nations tahun 2018 dengan Pilot Cities yaitu Jakarta, Indonesia berada pada peringkat 107 dengan level EGDI (E-Government Development Index) adalah High. Manfaat penerapan E-Government harus masyarakat yang merasakannya. Sebanyak apapun sistem yang dibangun jika manfaat tidak dapat dirasakan oleh masyarakat, maka cita-cita terwujudnya good governance tidak akan tercipta. Penelitian ini melakukan eksplorasi terhadap faktor yang dapat memberikan dampak terhadap penggunaan layanan e-government yang ada di Indonesia. Model yang digunakan untuk evaluasi faktor yaitu kolaborasi framework UTAUT dan GAM. Berdasarkan hasil penelitian yang telah dilakukan, faktor Performance Expectancy (PE), Perceived image (PI) dan Perceived functional benefit (PFB) memiliki pengaruh terhadap minat masyarakat untuk menggunakan E-Government.
S Sistem Pakar Diagnosa Gangguan Kejiwaan Menggunakan Metode Inferensi Forward Chaining dan Certainty Factor Fauzan, Muhammad; Wulandari, Fitri; Haerani, Elin; Oktavia, Lola
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3232

Abstract

The era of artificial intelligence AI technology is now an advantage because the system does all the work according to the human brain. Expert Systemis abranch ofartificial intelligencethat adapts the mind and reasoning of an expert to solve a problem and make a decision so that it draws conclusions based on the facts. From cases of psychiatric disorders, this expert system is highly recommended to make it easier to find out what type of disorder you are suffering from to assist the public and experts in diagnosing diseases quickly and accurately. For this reason, researcherscreated an expert system for diagnosingpsychiatric disordersusing the forwardchaining inferencemethod and certainty factor. Based on the results of the implementation and analysis thathave been carried out in this study, it produces a software system, namely an expert system that has an easy-to-understand display, and can assist experts in diagnosing psychiatric disorders
Pengukuran Tingkat Kematangan pada Pelayanan Akademik dengan Cobit 4.1 Menggunakan Domain Monitor dan Evaluate: Measuring the Level of Maturity in Academic Services with Cobit 4.1 Using the Monitor and Evaluate Domains Novriyanto, Novriyanto; Lesmana, Aria; Darmizal, Teddie; Oktavia, Lola
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1320

Abstract

Perkembangan teknologi informasi yang sangat pesat menjadikannya sebagai suatu kebutuhan yang penting untuk perkembangan suatu organisasi. Universitas Hang Tuah menjadi salah satu Lembaga yang menggunakan teknologi informasi dalm berbagai aktivitas pelayanan akademiknya. Proses pelayanan akademik di Universitas Hang Tuah masih memiliki berbagai kekurangan yaitu terkait sistem atau aplikasi, pelaporan kinerja TI, infrastruktur, dan sumber daya TI lainnya. Pada permasalahan yang terjadi di Universitas Hang Tuah akan dilakukannya pengukuran tingkat kematangan dan memberi rekomendasi  perbaikan, setelah mendapatkan hasil GAP antara tata kelola saat ini dengan tata kelola yang diinginkan sesuai ketentuan framework COBIT 4.1 berfokus pada domain Monitor and Evaluate. Pengumpula data pada penelitian ini menggunakan wawancara dan kuesioner yang telah dipilih berdasarkan RACI Chart. Hasil maturity level dari penelitian didapat nilai domain Monitor and Evaluate  adalah 3,625 , yang menunjukkan bawah Universitas Hang Tuah mendapatkan level maturity level 4 (Managed and Measureabel) terdapat kesenjangan GAP atau rata-rata menjadi 0,58. Kemudian, Universitas Hang Tuah dapat mengimplementasikan beberapa usulan. Di antara usulan yang diajukan adalah agar manajemen menilai teknik pelatihan yang diberikan kepada pengguna sistem sesuai dengan standar kebutuhan untuk mencapai tujuan universitas
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier Dermawan, Jozu; Yusra, Yusra; Fikry, Muhammad; Agustian, Surya; Oktavia, Lola
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7475

Abstract

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Desain UI/UX Aplikasi Manajemen Keuangan Pribadi Menggunakan Metode User Centered Design (UCD) Prameswari, Putri; Mai Candra, Reski; Affandes, Muhammad; Oktavia, Lola
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.567

Abstract

Peningkatan penggunaan smartphone di Indonesia mencapai 358,8 juta perangkat pada tahun 2023 telah mengubah cara masyarakat dalam melakukan berbagai aktivitas, termasuk transaksi keuangan seperti e-banking, e-commerce, dan e-learning. Meskipun kemudahan ini membawa banyak manfaat, tantangan baru juga muncul, salah satunya adalah perilaku konsumtif yang semakin meningkat, yang diperparah oleh rendahnya literasi keuangan di Indonesia, yang hanya mencapai 38,08% menurut Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) pada tahun 2019. Untuk mengatasi permasalahan ini, penelitian ini bertujuan merancang desain aplikasi manajemen keuangan yang dapat mendorong pengelolaan keuangan yang lebih baik. Metode yang digunakan dalam penelitian ini adalah User Centered Design (UCD), yang berfokus pada kebutuhan dan kenyamanan pengguna dalam proses perancangan aplikasi. Hasil penelitian menunjukkan bahwa pengujian desain UI/UX aplikasi menggunakan System Usability Scale (SUS) memperoleh skor sebesar 86, yang mengindikasikan penerimaan yang sangat baik dari pengguna. Temuan ini menyatakan bahwa penerapan metode UCD dalam merancang aplikasi manajemen keuangan terbukti efektif dalam meningkatkan kepuasan dan pengalaman pengguna, serta memberikan kontribusi positif terhadap pengelolaan keuangan pribadi.
Aplikasi Web Question Answering Menggunakan Langchain OpenAI Tentang Peraturan Perundang-undangan Bidang Pendidikan Saputra, Ikhsan Dwi; Harahap, Nazruddin Safaat; Agustian, Surya; Fikry, Muhammad; Oktavia, Lola
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6182

Abstract

In the rapid development of information technology over the past few years, the ease of accessing information has been one of the significant achievements. Artificial intelligence (AI) has emerged as a potential tool in bringing innovative solutions in various sectors of human life. This research aims to develop a web application capable of answering questions related to educational legislation using the LangChain framework and BERT model. The primary issue addressed is the complexity and volume of legal documents that are challenging for lay users to access and understand. The methodology involves converting legal documents from PDF to text, segmenting the text using LangChain, and evaluating system performance with BERTScore and ROUGE Score. The results indicate that BERTScore is superior in measuring the alignment between the system’s answers and reference answers, with some questions achieving a score of 100%. However, there are limitations, such as the manual effort required for document conversion and the substantial computational resources needed for text processing. This research significantly contributes to facilitating access and comprehension of educational legal documents and opens opportunities for further development with more advanced conversion techniques and AI models.
Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor (K-NN) Untuk Klasifikasi Penyakit Gagal Jantung Zahri, Firman; Insani, Fitri; Jasril, Jasril; Oktavia, Lola
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6480

Abstract

A condition known as heart failure, where the heart is unable to pump enough blood to meet the body's needs for oxygen and nutrients, should not be taken lightly. This can result in a number of symptoms, such as fatigue, fluid retention, and dyspnea. The World Heart Federation estimates that up to 1.8 million people in Southeast Asia suffered from heart failure in 2014. For prompt and efficient treatment, heart failure is a medical problem that needs to be identified. This disease has the potential to worsen if not treated immediately. Several machine learning methods can be used to help diagnose and categorize this disease. One of them is the popular algorithm, namely Naive Bayes and K-Nearest Neighbors. Naive Bayes is a simple but very efficient probability-based machine learning algorithm, especially in classification applications. K-Nearest Neighbors is comparing the data to be predicted with a number of its closest data in a feature space based on a certain distance, such as Euclidean distance, Manhattan, or others. This study was conducted using Confusion Matrix to evaluate and compare the Naive Bayes and K-Nearest Neighbor algorithms in the categorization of heart failure disease by collecting data totaling 918 heart failure patient data from kaggle. Based on the research findings, the K-Nearest Neighbor method achieved an accuracy score of 76%, while the Naive Bayes approach achieved 90% accuracy using a ratio of 80:20.
Analisa Perbandingan Metode Trend Moment dan Regresi Linear dalam Prediksi Kurs Mata Uang Rupiah terhadap Mata Uang Riyal Ananda, Rahmadan Alam Ardan; Nazir, Alwis; Oktavia, Lola; Haerani, Elin; Insani, Fitri
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7400

Abstract

Currency exchange rates play an important role in the economic stability of a country, especially in the context of international trade and global financial mobility. In Indonesia, fluctuations in the Rupiah exchange rate against the Saudi Arabian Riyal (SAR) have become a strategic issue, especially ahead of the Hajj season. This study aims to predict the exchange rate of Rupiah against Riyal in that period by using two forecasting approaches, namely Linear Regression and Trend Moment. The performance evaluation of both methods is conducted based on historical data using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indicators. The results show that Linear Regression provides a better level of accuracy with an MAE of 330.36 and a MAPE of 17.32%, compared to Trend Moment which has an MAE of 412.41 and a MAPE of 18.88%. This finding shows that Linear Regression is more effective in capturing the pattern of exchange rate changes that tend to be linear. The prediction results also show an increasing trend in the exchange rate ahead of the Hajj month, which correlates with the increasing demand for foreign exchange. The implications of these results can be utilized by prospective pilgrims, business actors, and the government in formulating more appropriate and adaptive financial strategies
Klasifikasi Sentimen Masyarakat Terhadap Revisi Undang-Undang Tentara Nasional Indonesia Menggunakan Naïve Bayes Classifier Abdul Haris Kurnia Sandi Harahap; Haerani, Elin; Oktavia, Lola; Okfalisa; Kurnia, Fitra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.615

Abstract

The revision of the Indonesian National Armed Forces Bill (RUU TNI) has become a hot topic in Indonesian public policy and has sparked controversy among the public due to its sudden emergence and lack of open planning process. This has raised concerns about the potential for military domination and the return of the dual function of the ABRI (Indonesian Armed Forces). The classification of public sentiment towards the RUU TNI is the focus of this study. Comments are categorized into two types of sentiment classes, namely positive and negative. The research stages include data collection, sentiment labeling, data cleaning, text normalization to lowercase letters, sentence or document segmentation into smaller parts, text data normalization, negation handling, stopword removal, and stemming, weighting using the TF-IDF technique, model classification development, and evaluation of the model's performance. The Naïve Bayes Classifier method classified 1,547 comment data points collected from two Instagram social media accounts. The Naïve Bayes Classifier model achieved an accuracy of 83.74%, precision of 81.17%, recall of 87.86%, and an F1-score of 84.38%. This study has limitations, including the limited amount of data collected. These include an imbalance in the amount of data between sentiment categories, data from only one social media platform, and the suboptimal identification of positive and negative sentiments. It is recommended that future research compare this method with other classification methods, expand the dataset, broaden the scope of data collection by involving various social media platforms over a wider time span, thereby providing a more comprehensive picture of public opinion, and test a wider range of algorithm combinations. This study can serve as an initial indicator for rapid policy evaluation, where positive or negative comments from the public on social media can provide important input in assessing the effectiveness of a policy.
Sentiment Analysis of X Application Users on Bitcoin Using the Naïve Bayes Method Optimized with Particle Swarm Optimization (PSO) Muhammad, Raja Allifin; Haerani, Elin; Wulandari, Fitri; Oktavia, Lola
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5390

Abstract

Advancements in technology and social media have significantly transformed the way individuals express their opinions—one of which is toward decentralized digital currencies that utilize blockchain technology to enable peer-to-peer transactions, such as Bitcoin. This study aims to evaluate user sentiment toward Bitcoin by implementing the Naïve Bayes method optimized with Particle Swarm Optimization (PSO), using data gathered from the X application (formerly Twitter). The data were collected through web scraping of user posts containing the keyword “Bitcoin.” Text preprocessing was performed to enhance data quality, followed by feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) approach to convert textual data into numerical representations. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Initial results show that the Naïve Bayes classifier performs well in sentiment classification. The integration of PSO as an optimization method improved classification performance from 66.14% to 69.14%. This study contributes to a deeper understanding of public opinion on Bitcoin and demonstrates the effectiveness of combining Naïve Bayes and PSO in text-based sentiment analysis.
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Adrian Topano Agustian, Surya Alfaridzy, M. Audi Ananda, Rahmadan Alam Ardan Anugrah, Wendy Darmizal, Teddie Dermawan, Jozu Dinanti, Sri Wahyu Edo Saputra, Riski Elin Haerani Fitri Insani Fitri Wulandari Hasanah, Qomariah Jasril Jasril kurnia, fitra Lesmana, Aria Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad, Raja Allifin Nazir, Alwis Nazruddin Safaat H Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Prameswari, Putri Reski Mai Candra Saputra, Ikhsan Dwi Wirayudha, Muhammad Aldi Yusra, Yusra Zahri, Firman zikrillah aulia, said rizki