cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tati Mardiana
Contact Email
jurnal.jri@kresnamediapublisher.com
Phone
-
Journal Mail Official
jurnal.jri@kresnamediapublisher.com
Editorial Address
-
Location
Kota banjar,
Jawa barat
INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 417 Documents
 21   22   23   24   25 
The Determination of Development Priorities Road Infrastructure at Dinas Pekerjaaan Umum dan Penataan Ruang Kabupaten Balangan Using AHP and Bayes Methods Haderiansyah Haderiansyah; Deni Mahdiiana; Ade Davy Wiranata; Mirza Sutrisno
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.549

Abstract

Dinas Pekerjaan Umum dan Penataan Ruang (PUPR) Kabupaten Balangan is a Regional Government Organization Unit (SOPD) that has the task of assisting the Bupati in administering government affairs in public works, infrastructure, and housing development. In addition, it also formulates, determines, and implements policies in the field of water resources management, road management, housing provision and development of residential areas, infrastructure financing, structuring of buildings, drinking water supply systems, wastewater management systems, and environmental drainage and waste, and construction services construction. Difficulty in determining priorities for infrastructure development on the Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Balangan, then an Infrastructure Development Priority Analysis system was created to support the construction of roads and bridges on Kabupaten Balangan. To determine the priority development weights using the AHP method and the order of priorities using the Bayes method because it is one of the techniques used to analyze the best decision-making from several alternatives to produce optimal gains. The results of the completed questionnaire get an average yield of 21 (twenty-one) or 87.5% (eighty-seven point five percent). If the average is included in the rating scale, get a VS rating or Very Satisfied. Decision support systems using AHP and Bayes methods can determine the priority of road infrastructure development at Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Balangan.
Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes Febryo Ponco Sulistyo; Endang Sri Palupi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.554

Abstract

Klasifikasi Faktor Blighted Ovum atau janin tidak berkembang dilakukan mengingat kasus Blighted Ovum banyak terjadi pada ibu hamil. Blighted Ovum merupakan 60% dari penyebab keguguran, di Indonesia ditemukan 37% dari setiap 100 kehamilan. Klasifikasi menggunakan Naïve Bayes berbasis Particle Swarm Optimization (PSO) yang hanya membutuhkan data training yang kecil untuk menentukan estimasi parameter yang diperlukan dalam proses pengklasifikasian dan penggunaan Particle Swarm Optimization dapat meningkatkan nilai akurasi serta memecahkan masalah optimasi. Dengan proses pemilihan data variable dan data attribute untuk membuat kuisioner sebagai metode pengambilan data. Hasil klasifikasi blighted ovum pada wanita hamil menggunakan algoritma Naïve Bayes dengan framework Rapid Miner mendapatkan nilai akurasi sebesar 71,56% dengan Area Under Curve (AUC) 0,674 termasuk dalam kategori klasifikasi yang baik. Setelah menggunakan optimasi PSO nilai akurasi naik menjadi 79,82% dengan Area Under Curve 0,764 termasuk kategori klasifikasi yang baik. Naïve bayes merupakan metode yang cocok untuk klasifikasi, dan PSO bisa membuat nilai akurasi dan AUC lebih baik lagi.
Analysis of Indonesian Language Dataset for Tax Court Cases: Multiclass Classification of Court Verdicts Ade Putera Kemala; Hafizh Ash Shiddiqi
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.555

Abstract

Tax is an obligation that arises due to the existence of laws, creating a duty for citizens to contribute a certain portion of their income to the state. The Tax Court serves as a judicial authority for taxpayers seeking justice in tax disputes, handling various types of taxes daily. This paper analyzes an Indonesian language dataset of tax court cases, aiming to perform multiclass classification to predict court verdicts. The dataset undergoes preprocessing steps, while data augmentation using oversampling and label weighting techniques addresses class imbalance. Two models, bi-LSTM and IndoBERT, are utilized for classification. The research produced a final result of the model with 75.83% using the IndoBERT model. The results demonstrate the efficacy of both models in predicting court verdicts. This research has implications for predicting court conclusions with limited case details, providing valuable insights for legal decision-making processes. The findings contribute to legal data analysis, showcasing the potential of NLP techniques in understanding and predicting court outcomes, thus enhancing the efficiency of legal proceedings.
Clickbait Detection in Indonesia Headline News Using IndoBERT and RoBERTa Muhammad Edo Syahputra; Ade Putera Kemala; Dimas Ramdhan Ramdhan
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.556

Abstract

This paper explores clickbait detection using Transformer models, specifically IndoBERT and RoBERTa. The objective is to leverage the models specifically for clickbait detection accuracy by employing balancing and augmentation techniques on the dataset. The research demonstrates the benefit of balancing techniques in improving model performance. Additionally, data augmentation techniques also improved the performance of RoBERTa. However, it resulted differently for IndoBERT with slightly decreased performance. These findings underline the importance of considering model selection and dataset characteristics when applying augmentation. Based on the result, IndoBERT, with a balanced distribution, outperformed the previous study and the other models used in this research. Furthermore, by incorporating balancing and augmentation techniques, the research surpasses previous studies, contributing to the advancement of clickbait detection accuracy. This work highlights the value of leveraging pre-trained Transformer models and specific dataset-handling techniques. The implications include the necessity of dataset balancing for accurate detection and the varying impact of augmentation on different models. These insights aid researchers and practitioners in making informed decisions for clickbait detection tasks, benefiting content moderation, online user experience, and information reliability. The study emphasizes the significance of utilizing state-of-the-art models and tailored approaches to improve clickbait detection performance.
A Study on Enhanced Spatial Clustering Using Ensemble DBscan and UMAP to Map Fire Zone in Greater Jakarta, Indonesia Silviya Hasana; Devi Fitrianah
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.557

Abstract

This research investigated ensemble clustering algorithms and dimensionality reduction for fire zone mapping, specifically DBSCAN + UMAP. We evaluated six clustering methods: DBSCAN, ensemble DBSCAN, DBSCAN + UMAP, ensemble DBSCAN + UMAP, HDBSCAN and Gaussian Mixture Model (GMM). We evaluated our results based on the Silhouette Score and the Davies-Bouldin (DB) index, emphasizing handling irregular cluster shapes, smaller clusters and resolving incompact clusters. Our findings suggested that ensemble DBSCAN + UMAP outperformed five other methods with zero noise clusters indicating clustering results are resistant to outliers, leading to a clearer identification of fire-prone areas, a high Silhouette Score of 0.971, indicating accurate cluster separation of distinct areas of potential fire hazards and an exceptionally low DB Index of 0.05 that indicates compact clusters to identify well-defined and geographically concentrated areas prone to fire hazards. Our findings contribute to the advanced techniques for minimizing the impacts of fires and improving fire hazard assessments in Indonesia.
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.558

Abstract

Flood disasters can occur at any time when the factors for the amount of river water discharge and rainfall intensity tend to be high, so preparations and ways of handling are needed to anticipate flood disasters quickly, precisely, and accurately for the Surabaya Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is by calculating predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict rainfall and river water discharge on the Jagir River in Surabaya. The LSTM method is a model commonly used for predictions based on time series data. The data obtained are rainfall data and water discharge on the Jagir River, Surabaya, which will be used as training and testing data to make predictions. The results of implementing the LSTM method using data training of 70% and data testing of 30% on rainfall data using the best epoch, namely at epoch ten by producing tests on data testing can have a Mean Absolute Error (MAE) performance of 4.5 and Root Mean Square Error (RMSE) of 9.7. Whereas the water discharge variable uses the best epoch, namely at epoch 75, by producing data testing data which can have a Mean Absolute Error (MAE) performance of 11.49 and a Root Mean Square Error (RMSE) of 9.63.
Ultra-Micro Lending Eligibility Support System With Exponential Comparison Method (MPE) Ninuk Wiliani; Herry Wahyono; Mulyana Adi Saputra
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.565

Abstract

The process of providing credit can now be done easily and closely through the presence of BRILink agents with additional facilities in addition to payment points, namely as partners of ultra-micro loans, which are now popularly called UMi Partners, where BRILink agents can distribute micro loans with a loan range of 1 to 5 million. This is done by management as a financial inclusion program and as a revitalization of work in all operational work units (UKO). This research uses the Exponential Comparison Method (MPE) to determine credit granting decisions to optimize all existing information systems by implementing a system that can be used and run by UMi partners to improve the process of providing creditworthiness to their partners. The system uses the NetBeans IDE with Java programming. The results of the calculations carried out by the system are manual calculations that have been carried out so that the results of this study can be applied properly so as to produce creditworthiness that helps the credit-granting process.
Identifying Skin Cancer Disease Types With You Only Look Once (YOLO) Algorithm Ninuk Wiliani; Nur Hikmah; Anita Putri Valeria
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.566

Abstract

The skin is the outermost vital organ and is susceptible to various diseases, including skin cancer. The number of cases of skin cancer around the world continues to increase every year, including in Indonesia. Proper handling is very important to cure skin cancer, and one of the solutions that can be used is the Deep Learning method. This study aims to apply the Deep Learning method, specifically an object detection algorithm called You Only Look Once (YOLO), for early skin cancer detection. The YOLOv5s algorithm was chosen as the model for this study because it has good accuracy and can detect objects in real-time. The research method involved collecting data on skin cancer cases and training the YOLOv5s model. After training, model testing was carried out to evaluate the ability to detect skin cancer. The test results show that the YOLOv5s model has an accuracy of 89.1% in detecting skin cancer types. This research has important implications in the health sector, especially in early skin cancer detection.
Improving The Performance of the K-Nearest Neighbor Algorithm in the Selection of KIP Scholarship Recipients Manzilur Rahman Romadhon; M. Faisal; M. Imamudin
Jurnal Riset Informatika Vol 5 No 4 (2022): Periode September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.575

Abstract

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are indeed eligible for KIP scholarships, it is necessary to classify scholarship recipients with data mining classification techniques correctly. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.
Combination of Profile Matching and SAW Methods for College KIP Admission Riya Majalista; M. Izman Herdiansyah; Zaid Amin
Jurnal Riset Informatika Vol 5 No 4 (2022): Periode September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.576

Abstract

The KIP College program at Baturaja University has been running since 2020. The large number of people interested in this program has made the university that runs this program have difficulty making decisions about recipients of the KIP college program. The data is on interested participants in the KIP program studying at Baturaja University (UNBARA). The gap between the quota determined by the Ministry of Education, Culture, Research, and Technology and the number of registrants triggers difficulties for management in making decisions. This research aims to analyze the KIP Kuliah program selection results using the combination of Profile Matching and SAW methods. From the analysis of determining criteria and rankings using the Combination Method of Profile Matching and SAW, the results show the names of students who will occupy the UNBARA KIP program quota. The result of data calculations already obtained a value of 1,96667 with alternative data A208 in the name of Randi. Alternative A208 can be recommended as the recipient of the College KIP because it has the profile most appropriate to the specified criteria. So, it can be concluded that SPK, using the combination of Profile Matching and SAW methods, can be applied as a form of recommendation in decision-making in determining UNBARA KIP college program recipients

Page 23 of 42 | Total Record : 417

 21   22   23   24   25 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol. 7 No. 4 (2025): September 2025 Vol. 7 No. 3 (2025): Juni 2025 Vol. 7 No. 2 (2025): Maret 2025 Vol. 7 No. 1 (2024): December 2024 Vol. 6 No. 4 (2024): September 2024 Vol. 6 No. 3 (2024): June 2024 Vol. 6 No. 2 (2024): March 2024 Vol. 6 No. 1 (2023): December 2023 Vol. 5 No. 4 (2023): September 2023 Vol. 5 No. 3 (2023): June 2023 Vol 5 No 3 (2023): Priode of June 2023 Vol 5 No 2 (2023): Priode of March 2023 Vol. 5 No. 2 (2023): March 2023 Vol 5 No 4 (2022): Periode September 2023 Vol 5 No 1 (2022): Priode of December 2022 Vol. 5 No. 1 (2022): December 2022 Vol. 4 No. 4 (2022): September 2022 Vol 4 No 4 (2022): Period of September 2022 Vol 4 No 3 (2022): Period of June 2022 Vol. 4 No. 3 (2022): June 2022 Vol 4 No 2 (2022): Period of March 2022 Vol. 4 No. 2 (2022): March 2022 Vol 4 No 1 (2021): Period of December 2021 Vol. 4 No. 1 (2021): December 2021 Vol 3 No 4 (2021): Period of September 2021 Vol. 3 No. 4 (2021): September 2021 Edition Vol. 3 No. 3 (2021): June 2021 Edition Vol 3 No 3 (2021): Period of June 2021 Vol. 3 No. 2 (2021): March 2021 Edition Vol. 3 No. 1 (2020): December 2020 Edition Vol. 2 No. 4 (2020): Period September 2020 Vol. 2 No. 3 (2020): June 2020 Edition Vol. 2 No. 2 (2020): March 2020 Edition Vol. 2 No. 1 (2019): Periode Desember 2019 Vol. 1 No. 4 (2019): Periode September 2019 Vol 1 No 4 (2019): Periode September 2019 Vol. 1 No. 3 (2019): Periode Juni 2019 Vol 1 No 2 (2019): Periode Maret 2019 Vol. 1 No. 2 (2019): Periode Maret 2019 Vol. 1 No. 1 (2018): Periode Desember 2018 More Issue