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All Journal JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Siti Ramadhani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Penerapan Metode Clustering Dalam Pengelompokan Kasus Perceraian Pada Pengadilan Agama di Kota Pekanbaru Menggunakan Algoritma K-Medoids Satria Bumartaduri; Siska Kurnia Gusti; Fadhilah Syafria; Elin Haerani; Siti Ramadhani
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5560

Abstract

Divorce is the breaking of a husband and wife relationship from a marriage. When a couple does not want to continue their marriage relationship, one of the factors causing divorce is that the husband and wife do not carry out their duties properly. Divorce cases also occur in the city of Pekanbaru and have increased from 2020 to 2022. In connection with this problem, researchers conducted research with the aim of classifying districts in Pekanbaru that have the most divorces. The method used in this study is K-Medoids Clustering, because this method can divide a dataset into several groups. The advantage of this method is that it can overcome the weaknesses of the K-Means algorithm which are sensitive to outliers. The tests used in this study use the RapidMiner tools and the Davies Bouldin Index to ensure cluster accuracy. Attributes used in this research are region/regency, age difference between spouses, plaintiff's and defendant's education, and reasons for divorce. The results of this study can be used as information for the government to reduce the divorce rate in the city of Pekanbaru so that appropriate programs can be developed for each sub-district in overcoming the divorce rate in Pekanbaru. From testing using the K-Medoids algorithm, the cluster results obtained showed that the highest divorce rate was in cluster 1 with 565 items, while cluster 2 had 395 items and cluster 3 had 288 items. The results of the study show that the use of 3 clusters is the best cluster with a DBI value of 0.884.
Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit Risfi Ayu Sandika; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3359

Abstract

The production of palm kernel is a significant product for the company and plays a crucial role. Nevertheless, the stability of kernel production is not always consistent, and the quality of the kernel can be detrimental to the company. As consumer demands change over time, companies must anticipate every fluctuation in palm kernel production. Hence it is vital to figure the long run with a settlement prepare utilizing information mining utilizing information within the past. The Triple Exponential Smoothing and Double Moving Average methods, which are data mining methods for future forecasting, were used in this study. The aim of this research is to predict the yield of future oil palm kernel production using the Triple Exponential Smoothing and Double Moving Average methods and to determine the level of forecasting errors using the Mean Absolute Percentage Error (MAPE) method. The data for the last ten years, from January 2013 to December 2022, were used in this study. After testing the Triple Exponential Smoothing method with parameters α=0.2,β=0.γ=0.2, the error rate using MAPE was 9.48%, and the Double Moving Average method had an error rate of 11.2%. The MAPE results of the Triple Exponential Smoothing method are considered very good, while the MAPE results of the Double Moving Average method are categorized as good based on the range of MAPE values. This research is expected to provide information to related companies as a supporting reference in anticipating palm oil kernel production. The conclusion of the research is that the Triple Exponential Smoothing method with the test parameters is the best method for forecasting.
Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Contrast Stretching Pada CNN dengan EfficientNet-B0 Alfitra Salam; Febi Yanto; Surya Agustian; Siti Ramadhani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1448

Abstract

Data from the World Health Organization (WHO) indicates that in 2020, approximately 10 million people died from cancer. Smoking has been identified as a primary factor causing lung cancer, as cigarettes contain over 60 toxic substances that can trigger the development of the disease. The rate of lung cancer has rapidly increased due to excessive cigarette consumption. Detecting nodules in the lungs typically takes about 10-30 minutes. In this study, a Convolutional Neural Network (CNN) algorithm with EfficientNet-B0 architecture is employed to classify lung cancer. The preprocessing process involves contrast stretching, and various hyperparameter optimization techniques such as Adam, Adagrad, and SGD are used to enhance the CNN's performance. Average pooling with output dense layers of 64, 32, 16, 1 is utilized. Performance analysis is conducted using a confusion matrix. The highest classification results are achieved using the ADAM optimizer with a learning rate of 0.01, where accuracy reaches 72.48%, precision is 71.52%, recall is 64.2%, and the F1 score is 64.76%. Meanwhile, results obtained from the original dataset show differences. The highest classification result is obtained using the ADAM optimizer with a learning rate of 0.01, achieving an accuracy of 64.22%, precision of 52.69%, recall of 50.52%, and an F1 score of 43.51%. These results indicate that the use of contrast stretching in lung cancer classification preprocessing is highly effective in improving accuracy
Klasifikasi Data Penerimaan Zakat dengan Algoritma K-Nearest Neighbor Alfin Hernandes; Siska Kurnia Gusti; Fadhilah Syafria; Lestari Handayani; Siti Ramadhani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1528

Abstract

National Amil Zakat Agency (BAZNAS) is an institution responsible for managing zakat established by the government. BAZNAS has a presence in every district or city, and one of them is the BAZNAS in the city of Pekanbaru. BAZNAS in Pekanbaru city is responsible for distributing zakat to various empowerment programs, one of which is the Pekanbaru Cares program. Currently, BAZNAS in Pekanbaru city is facing issues related to the method of distributing zakat, where the process of determining the criteria for zakat recipients is still being done manually by the committee of BAZNAS in the city of Pekanbaru. This condition is considered inefficient and poses one of the challenges that need to be addressed. To overcome the mentioned constraints, steps are needed to improve the effectiveness and efficiency of data collection for potential zakat recipients. One of the solutions is to implement a classification system to facilitate the data collection process, using the K-Nearest Neighbor (KNN) method. This approach functions as a tool to classify data for potential beneficiaries. This research aims to classify data and measure the accuracy in assessing the eligibility of zakat recipients based on predetermined criteria, utilizing the K-Nearest Neighbor (K-NN) algorithm. A total of 602 data from BAZNAS in the city of Pekanbaru were used in this study, by dividing the training and test data, namely divided 90:10, 80:20, and 70:30 splits. The evaluation results from the confusion matrix of k=3, k=5, k=7, k=9, and k=11 show that the highest accuracy is achieved at k=5 with an 80:20 split, with an accuracy rate of 89.3%. Furthermore, a precision of 87.3% and a recall of 91.4% can also be attained through this approach.
Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit Raja Indra Ramoza; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3433

Abstract

Oil palm plays a crucial role in agriculture and plantations in Indonesia as a commodity with high economic potential. Net Fresh Fruit Bunches (FFB) production is an essential desired outcome in an oil palm plantation. Net FFB is utilized as the primary raw material for the production of Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). The existing challenge is that companies seek to achieve precise quantities and timing for net FFB production in oil palm. One proactive measure to address this is by predicting the net FFB production. Therefore, the objective of this research is to forecast net FFB production by comparing triple exponential smoothing and fuzzy time series methods. Data processing results demonstrate that both forecasting methods yield excellent quality predictions for net FFB production. In the conducted testing, both methods achieved low forecast error values, with MAPE of 11.14670196% and 10.44596891% respectively. However, fuzzy time series exhibited a lower error value compared to the triple exponential smoothing method. Based on these findings, it can be concluded that fuzzy time series is the most reliable model for accurately predicting net FFB production. The advantage of fuzzy time series in forecasting net FFB production can provide significant benefits for companies in determining appropriate strategies for future planning.
Klasifikasi Kondisi Janin Berdasarkan Data Kardiotogram Menggunakan Algoritma Naive Bayes Isruel Syah Utama; Elin Haerani; Fitri Wulandari; Siti Ramadhani
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.
Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra Yusra; Muhammad Fikry; Surya Agustian; Siti Ramadhani
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.v5i4.626

Abstract

Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
Implementasi Regresi Linier Berganda Untuk Prediksi Harga Mobil Bekas Di Indonesia Berbasis Gradio M Ridho Alfani; Elvia Budianita; Lestari Handayani; Siti Ramadhani
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1097

Abstract

The price of a used vehicle depends on various aspects that cause changes in the selling value in the market, such as model, year, transmission, mileage, fuel, tax, mpg, and cc. A common problem in used car transactions is determining prices that are still not fully based on measurable data analysis. The purpose of this study is to design a model to estimate the price of a used car through the multiple linear regression method and implement it in the User Interface. The data used in this study is secondary data obtained from the Kaggle public repository, and collected from several used car buying and selling forums in Pekanbaru and social media platforms such as Facebook that contain vehicle price information. The dataset contains 400 rows of data with a range of car years from 2005 to 2025. The research stages include data preprocessing in the form of categorical variable encoding and data normalization. Data is divided into training data and testing data, followed by the process of model building and model performance assessment. Evaluation is carried out using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The model was built using several independent variables, namely model, year, transmission, kilometer, fuel, tax, mpg, and cc, with vehicle price as the dependent variable. Based on the test results, the multiple linear regression method shows the ability to produce used car price estimates and has potential for application in decision support systems. The test results show that the MSE value on the training data is 0.004 and the testing data is 0.010, MAE on the training data is 0.046 and the testing data is 0.071, and RMSE on the training data is 0.062 and 0.100 on the testing data, the coefficient of determination (R²) on the training data is 0.985 and on the testing data is 0.955. The next model is implemented using the Python Gradio library so that users can predict vehicle prices through the User Interface.
Prediksi Saham Berdasarkan Data Teknikal Serta Fundamental Menggunakan Algoritma XGBoost Yoga Nur Pradana; Fitri Insani; Jasril Jasril; Siti Ramadhani
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1126

Abstract

The capital market has an important role in the economy as a means of investment and fundraising, with banking sector stocks being one of the main contributors to market capitalization in Indonesia. However, the investment decision-making process often faces obstacles in the form of limited investors' ability to comprehensively analyze fundamental and technical data, as well as irrational behavior that causes decisions to be less than optimal. This conditions encourage the need for a more objective and data-driven approach to help predict stock price movements. The results of the model evaluation on the test data showed excellent performance: BCA obtained a MAPE of 2.8% and an R² of 0.9488; BNI with MAPE 3.06% and R² 0.8863; Bank Mandiri with a MAPE of 4.70% and R² 0.9114; and BRI with MAPE of 2.48% and R² 0.8872. Based on this model, the results of the share price prediction for 2026 show that BCA is predicted to experience a significant increase from IDR 7,756 (January) to IDR 7,846 (June), while Bank Mandiri is predicted to grow from IDR 5,211 (January) to IDR 5,930 (June). BNI and BRI are predicted to experience an increase in share prices, respectively from IDR 3,327 (January) to IDR 3,683 (June) and from IDR 4,124 (January) to IDR 4,541 (June). This research contributes by presenting a stock prediction model that combines technical and fundamental data at once, applied to four major Indonesian banks Bank Central Asia, Bank Rakyat Indonesia, Bank Mandiri, dan Bank Negara Indonesia in a single modeling framework. This approach has proven to produce good accuracy with an average MAPE of 3.13% and R² 0.919, as well as being a more objective alternative for investors in analyzing stock price movements. However, the prediction results obtained in this study are analytical tools and are not intended as direct investment recommendations.
Co-Authors Alfin Hernandes Alfitra Salam Elin Haerani Elvia Budianita Fadhilah Syafria Febi Yanto Fitri Insani Fitri Wulandari Gusti, Siska Kurnia Isruel Syah Utama Jasril Jasril Lestari Handayani M Ridho Alfani Muhammad Fikry Muhammad Iqbal Maulana Raja Indra Ramoza Risfi Ayu Sandika Satria Bumartaduri Surya Agustian Yoga Nur Pradana Yusra Yusra