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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Sains dan Informatika Jurnal Nasional Komputasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Computer Science Research
Fitri Wulandari
Fakultas Sains Dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Neuroscience Transportation Other

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Penerapan Algoritma Naïve Bayes Terhadap Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Amelia Irsyada; Elin Haerani; Muhammad Irsyad; Fitri Wulandari; Liza Afriyanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7203

Abstract

Poverty in Indonesia is one of the complex social issues. As a manifestation of the government's concern about poverty in the country, various assistance programs have been established to target the impoverished population. One such program aimed at alleviating poverty in Indonesia is the Family Hope Program (Program Keluarga Harapan or PKH). PKH is a conditional cash transfer program provided to the impoverished community. The manual selection process for aid recipients is considered less than ideal, leading to issues of improper distribution. In this study, the Naïve Bayes algorithm is applied to classify PKH aid recipients in the Bungaraya Subdistrict, Siak Regency, as part of the government's efforts to tackle poverty. The dataset used consists of 560 records, including data on existing PKH aid recipients and potential recipients from various villages in the Bungaraya Subdistrict for the year 2022. The attributes considered in this research include age, income, number of dependents, dependents attending school, dependents with disabilities, housing status, floor type, and wall type. The highest accuracy obtained through calculations on Google Colab is 99% for an 80:20 ratio, while the accuracy obtained using RapidMiner is 94%.
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.
Klasifikasi Kondisi Janin Menggunakan Algoritma K-Nearest Neighbors dan Teknik SMOTE Berdasarkan Data Kardiotogram Dede Fadillah; Elin Haerani; Fitri Wulandari; Fadhilah Syafria
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.
Aplikasi Pemesanan Online Kue Tradisional Berbasis Web Menggunakan Metode Rapid Aplication Development Fitri Wulandari; Adhitya Pramata
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6673

Abstract

Traditional cakes are one of Indonesia's culinary cultural heritages that need to be preserved. However, small business actors, especially MSMEs, often face obstacles in reaching consumers widely due to limited marketing access. This study aims to design and build a website-based traditional cake online ordering application using the Rapid Application Development (RAD) method. This method was chosen because of its iterative approach and direct user involvement, so that application development can be carried out quickly and according to needs. This application is designed to provide key features such as product catalogs, online ordering systems, and digital payments. Web technology is used so that the application can be accessed through various devices, both desktop and mobile. The development process includes four stages: needs planning, design, planning, and implementation. The test results show that the application has achieved a 100% functional success rate, with all key features running smoothly. In addition, User Acceptance Testing (UAT) testing using the Likert scale obtained an average score of 4.5 out of 5, reflecting a very high level of user satisfaction with ease of use, ordering, and product completeness. This application is considered effective in supporting the digitalization of micro, small, and medium enterprises (MSMEs) and preserving local culinary through technological innovation. This application not only makes it easier for consumers to order traditional cakes online, but also helps MSMEs manage orders and expand market reach. This research contributes to supporting the digitalization of MSMEs, especially in the traditional culinary sector, and has the potential to be a solution to maintain the existence of traditional cakes in the digital era.
Co-Authors Adhitya Pramata Alwis Nazir Amany Akhyar Amelia Irsyada Dede Fadillah Dimas Rifqi Ekaryanto Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Fadhilah Syafria Farhan Adib Surya Febi Nur Salisah Fitri Insani Gusti, Siska Kurnia Isruel Syah Utama Liza Afriyanti Muhammad Affandes Muhammad Irsyad Muhammad Ridho Amrullah Nurul Ikhsan Reski Mai Candra Rice Novita Rinto Setiawan Siti Ramadhani Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Andika Yusra