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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Krea-TIF: Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research JUTECH : Journal Education and Technology Journal of Comprehensive Science Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Innovation Multidisipliner Research Indonesian Journal of Innovation Multidisipliner Research INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Elin Haerani
UIN Suska Riau
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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 Kondisi Janin Berdasarkan Data Kardiotogram Menggunakan Algoritma Naive Bayes Syah Utama, Isruel; Haerani, Elin; Wulandari, Fitri; Ramadhani, Siti
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.v5i4.584

Abstract

Fetal health during pregnancy is a crucial aspect that can be monitored through cardiotocography (CTG) data; however, manual interpretation of this data often encounters challenges due to class imbalance. This study aims to develop a fetal condition classification model using the Naive Bayes algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE) to address the disparity in class distribution. The CTG dataset, obtained from Kaggle, consists of 2,126 records categorized into three target classes: Normal, Suspect, and Pathological. Data processing followed the Knowledge Discovery in Databases (KDD) framework, including data selection, cleaning, normalization, splitting into four ratios (70:30, 80:20, 85:15, and 90:10), SMOTE application, and model evaluation using accuracy and F1-Macro metrics. The results showed that the 80:20 ratio yielded the highest accuracy at 79.81%, while the 90:10 ratio produced the highest F1-Macro score of 0.6788. These findings indicate that although accuracy remained relatively stable, the F1-Macro metric provided a better representation of performance across all classes, especially minority ones. The application of SMOTE proved effective in balancing class distribution and enhancing model sensitivity. This study serves as a foundational step in developing a more reliable and adaptive fetal condition classification system and highlights opportunities for further exploration of alternative algorithms and SMOTE parameter optimization.
Klasifikasi Kondisi Janin Menggunakan Algoritma K-Nearest Neighbors dan Teknik SMOTE Berdasarkan Data Kardiotogram Dede Fadillah; Haerani, Elin; Wulandari, Fitri; Syafria, Fadhilah
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.v5i4.585

Abstract

Fetal health is a crucial aspect in reducing infant mortality rates, where cardiotocography (CTG) is used to monitor fetal condition through recordings of fetal heart rate and uterine contractions. However, manual interpretation of CTG data still faces challenges, particularly due to imbalanced class distribution. This study aims to develop a classification model for fetal conditions using the K-Nearest Neighbors (K-NN) algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset used, sourced from Kaggle, consists of 2,126 CTG examinations categorized into three classes: Normal, Suspect, and Pathological. The data processing follows the Knowledge Discovery in Databases (KDD) process, including data selection, cleaning, normalization, splitting, balancing with SMOTE, and classification using K-NN. The model was evaluated using four training-testing split ratios (70:30, 80:20, 85:15, and 90:10) with accuracy and macro F1-score as metrics. The results indicate that the 85:15 split ratio achieved the highest accuracy of 89.7%, while the 90:10 ratio yielded the highest macro F1-score of 0.83. These findings suggest that the 85:15 ratio offers an optimal balance between model training and evaluation, whereas the highest F1-score at 90:10 reflects greater model sensitivity to minority classes. The combination of K-NN and SMOTE proved effective in addressing data imbalance and supports model stability in the overall classification process of fetal conditions.
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.
Implementasi Fuzzy Sugeno Berbasis IoT untuk Peringatan Kualitas Air Akuarium Ikan Mas Koki Rahman, Muhammad Taufikur; Yanto, Febi; Haerani, Elin
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.v5i4.627

Abstract

The manual monitoring of aquarium water quality is often ineffective due to time constraints and the potential delays in detecting critical parameter changes that can threaten fish health. This research develops a real-time water quality monitoring system for goldfish aquariums based on the Internet of Things (IoT) using the Sugeno fuzzy logic method. The system utilizes an Arduino Uno R4 WiFi microcontroller to process data from turbidity, Total Dissolved Solids (TDS), and water temperature sensors. The Sugeno fuzzy method is chosen for its ability to produce precise numerical outputs based on fuzzy rules. To assess water quality, the sensor data undergoes fuzzification, rule evaluation, implication/aggregation function application, and defuzzification stages. The measurement results are then processed in real-time and sent via WiFi connection to the Blynk application, which serves as a monitoring medium and sender of warning notifications to users when water quality falls outside safe limits, while information is also displayed on the OLED screen of the system. Water quality assessment is classified based on fuzzy output values into several condition categories: 0-20 (Very Good), 21-40 (Good), 41-60 (Fair), 61-80 (Poor), 81-100 (Very Poor). Based on the test results, the system has been proven to effectively detect and classify water quality conditions with high accuracy, as well as provide effective warning notifications. This system is expected to assist aquarium owners in maintaining optimal environmental conditions for the health of goldfish in an automatic, sustainable, and efficient manner.
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.
Clustering Data Penduduk Menggunakan Algoritma K-Means Ikhsan, Tomi; Haerani, Elin; Wulandari, Fitri; Syafria, Fadhilah
TIN: Terapan Informatika Nusantara Vol 5 No 12 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i12.7328

Abstract

Economic inequality is still a crucial factor facing Indonesia today, from big cities to remote villages, economic inequality is still a major problem. Bina Baru Village is no exception, a village inhabited by 5,760 people with a total of 1,742 families, spread across 30 neighbourhood associations (RT) and 8 community associations (RW). Various efforts are made to overcome the problem of economic inequality, one of which is by channeling assistance or providing policies that are right on target. One of the steps to overcome this problem is to group population data in Bina Baru village using the K-Means Clustering method which aims to determine the economic level of families in the region, so that local governments can more accurately make policies on the problem of economic inequality that occurs. The data used comes from a questionnaire of 1,005 family data with 64 attributes and 1,005 individual data with 84 attributes. The application of the k-means algorithm is carried out using python, also using DBI (Davies-Bouldin Index) to determine the optimum k value. In this study, the optimal k value is 3 clusters. Based on testing, it is found that Cluster 0 represents households with medium economic conditions, cluster 1 represents groups with better economic conditions and Cluster 2 is a group of households with low economic conditions. By clustering the population's economy, it is hoped that it can help stakeholders to provide targeted policies.
Clustering Data Penduduk Desa Menggunakan Algoritma Mean Shift Maulani, Tedi; Haerani, Elin; Wulandari, Fitri; Oktavia, Lola
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7550

Abstract

Social welfare remains a serious challenge in Indonesia, including in Riau Province, which, despite its abundant natural resources, still struggles with unequal distribution of welfare. One of the government’s efforts to address this issue is through social assistance programs. However, identifying the right beneficiaries remains problematic. This study aims to cluster residents of Desa Bina Baru using the Mean Shift algorithm to support more targeted social aid distribution. The clustering results were evaluated using the Silhouette Score to measure their quality. The optimal clustering was achieved at a quantile of 0.9, with the highest Silhouette Score of 0.5747, producing nine clusters with varying socioeconomic characteristics. Based on the analysis, clusters 2, 1, 5, and 6/7 were identified as the most eligible groups to receive government aid due to economic pressure, high number of dependents, and inadequate housing conditions. This prioritization is crucial for more accurate, data-driven distribution of aid and provides valuable insights to support sustainable poverty alleviation strategies in Desa Bina Baru.
Clustering Keluarga Miskin Desa Bina Baru dengan Metode K-Medoids Amelia, Felina; Iskandar, Iwan; Gusti, Siska Kurnia; Haerani, Elin; Yusra, Yusra
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 1 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i1.14104

Abstract

Kemiskinan di Indonesia terjadi di berbagai daerah, mulai pedesaan hingga perkotaan memiliki permasalahan kemiskinan masing – masing. Masalah kemiskinan juga dialami oleh Desa Bina Baru. Desa Bina Baru yang memiliki jumlah penduduk sebanyak 5.760 jiwa dengan total 1.742 keluarga, yang tersebar dalam 30 Rukun Tetangga (RT) dan 8 Rukun Warga (RW). Upaya dalam penurunan angka kemiskinan dapat dilakukan dengan berbagai cara, mulai pembangunan yang merata, penyaluran bantuan yang tepat sasaran, pemberian kebijakan yang tepat, dan lain sebagainya. Pengelompokan kemiskinan menjadikan salah satu upaya untuk menurunkan angka kemiskinan agar dapat memberikan informasi kepada pemerintahan daerah dalam memberikan kebijakan yang lebih tepat guna. Clustering merupakan teknik data mining yang bertujuan untuk mengelompokkan objek-objek data menjadi beberapa Cluster. Pada penelitian ini pengelompokkan dilakukan dengan teknik pengolahan data mining dengan algoritme K-Medoids dari data Desa Bina Baru tahun 2020 berjumlah 1.005. Hasil perbandingan perhitungan untuk Cluster 1 (kaya) sebanyak 527 penduduk, Cluster 2 (menengah) sebanyak 248 penduduk, dan Cluster 3 (miskin) sebanyak 225 penduduk, Hasil evaluasi dari algoritme k-Medoids adalah 0,991 yang menunjukan cluster yang dibentuk memberikan pengelompokan informasi yang baik. Hasil pengelompokan ini dapat dijadikan acuan untuk informasi kelompok keluarga miskin yang diperlukan pemerintah agar bantuan yang diberikan tepat sasaran.
Perbandingan Metode Naive Bayes Classifier dan Support Vector Machine dalam Analisis Sentimen Terhadap Pemilihan Presiden 2024 Prananda, Alga; Haerani, Elin; Fikry, Muhammad; Yanto, Febi
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 2 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i2.15364

Abstract

Salah satu sarana masyarakat untuk memberikan pendapat atau opini adalah menggunakan media sosial, khususnya youtube. Pada penelitian ini berfokus melakukan analisis sentimen terhadap Pemilihan Presiden 2024 dengan tiga kelas dan 2000 data opini, mendapatkan 875 kelas positif, 577 negatif, dan 548 netral. Tahapan penelitian melibatkan pengumpulan data, pre-processing (case folding, tokenizing, filtering, stemming), klasifikasi, pengujian, dan evaluasi. Juga melakukan perbandingan antara metode Naive Bayes Classifier (NBC) dan Support Vector Machine (SVM), menunjukkan bahwa SVM mendapat akurasi lebih baik dari NBC di setiap tipe pembagian kelas. Selain itu, hasil analisis sentimen menggunakan empat kata kunci menunjukkan dominasi sentimen positif terhadap Anies Baswedan (80.54%), Prabowo Subianto (64.76%), Calon Presiden secara umum (33.91%), dan Ganjar Pranowo (36.17%). Sentimen negatif cenderung tinggi untuk Ganjar Pranowo (51.42%) dan Prabowo Subianto (25.99%), sementara Anies Baswedan dan Calon Presiden memiliki tingkat sentimen negatif yang lebih rendah (16.53% dan 25.22%). Sentimen netral tercatat pada Prabowo Subianto (9.25%), Ganjar Pranowo (12.41%), Calon Presiden secara umum (40.87%), dan Anies Baswedan (2.93%).
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Afrianti, Liza Afriyanti, Liza Akbar, Lionita Asa Alfaridzy, M. Audi Amelia, Felina Ananda, Rahmadan Alam Ardan Anugrah, Wendy Baehaqi Cahyadi, Wahyu Dede Fadillah Elsilaturrahmi, Elsilaturrahmi Elvia Budianita Fadhilah Syafria Fahmi Rizal Febi Yanto Fitri Insani Fitri Wulandari Fitria Novitasari Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Habibi, M. Ilham hariansyah, Aldi Ikhsan, Tomi Indrizal, Habibi Putra Irsyada, Amelia Israldi, Tino Iwan Iskandar Jasril Jasril Kasman Rukun kurnia, fitra Mai Candra , Reski Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad Irsyad Muhammad Taufiq Muhammad Yusuf Fadhillah Muhammad, Raja Allifin Nazir, Alwis Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Oktavia, Lola Prananda, Alga Pranata, Joni Rahman, Muhammad Taufikur Ramadhani, Siti Rendi Ibnu Syahputra Sapitri, Janaria Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syah Utama, Isruel Vitriani, Yelfi Vitriani, Yelvi Wahyuni, Ayu Sri Yusra, Yusra zikrillah aulia, said rizki