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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Journal of Information Systems Engineering and Business Intelligence JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Kilat JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotekmesin Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Infotek : Jurnal Informatika dan Teknologi Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Bulletin of Computer Science Research Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Telematika MKOM Jurnal Pengabdian Masyarakat Nasional
Juanita, Safitri
Program Studi Sistem Informasi,Fakultas Teknologi Informasi, Universitas Budi Luhur
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Aplikasi Electronic Customer Relationship Management (E-CRM) untuk Meningkatkan Layanan Orang Tua pada Madrasah Aliyah Al-Falah Fadil Muhammad Zuhri; Safitri Juanita
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.9439

Abstract

Competition between private schools in New Student Admissions (PSB) requires the right strategy, namely implementing Electronic Customer Relationship Management (E-CRM). Madrasah Aliyah (MA) Al-Falah is currently having a problem with decreasing the number of prospective new students (CSB) during the pandemic because the CSB registration process is carried out offline, and brochures are distributed when CSB comes to school. Apart from that, there is no media for alumni to provide positive reviews or for students' parents to give criticism and suggestions. Therefore, this research contributes to designing an E-CRM application to increase the number of CSB and student-parent services at MA Al-Falah. This research aims to simplify the CSB admission process by implementing two stages of CRM, namely acquiring and retaining, to increase the loyalty of parents so that their children return to school at MA Al-Falah and provide solutions in terms of school promotions, alumni reviews, and criticism of suggestions from students' parents. The method used in this research is qualitative research with descriptive analysis and the Waterfall system development method. This research concludes that the E-CRM application at MA Al-Falah helps make the PSB process easier because it features a registration form, registration fee information and discount coupons. The E-CRM system can also increase the loyalty of students' parents because it has a suggestion and criticism feature.
PREDIKSI JUMLAH TENAGA KERJA ASING DI JAWA BARAT MENGGUNAKAN PERBANDINGAN ALGORITMA SUPPORT VECTOR REGRESSION DAN DECISION TREE REGRESSION Farill Andika Wardana; Safitri Juanita
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6003

Abstract

Beberapa tahun ini, Indonesia sedang menghadapkan masalah mengenai peningkatan jumlah tenaga kerja asing yang masuk ke Indonesia, salah satunya di provinsi Jawa Barat. Sehingga diperlukan model prediksi untuk memprediksi jumlah tenaga kerja asing yang masuk di provinsi Jawa Barat. Metode yang digunakan pada penelitian ini adalah CRISP-DM, dengan menggunakan dataset jumlah tenaga kerja asing di Jawa Barat periode 2014-2023, dan pada tahap pemodelan membandingkan 2 algoritma yaitu Decision Tree Regression (DTR) dan Support Vector Regression (SVR) dengan metode pengujian Cross-Validation (CV). Hasil pengujian performa kedua algoritma menggunakan Mean Squared Error (RMSE) dan Mean Absolute Error (MAE). Penelitian ini bertujuan untuk menemukan model peramalan untuk melakukan prediksi terhadap jumlah tenaga kerja asing yang masuk di provinsi Jawa Barat. Kesimpulan penelitian ini adalah model prediksi yang memiliki performa lebih unggul adalah Decision Tree Regression (DTR) dengan nilai RMSE sebesar 78.04% dan MAE sebesar 69.57%, sedangkan Support Vector Regression (SVR) hanya memiliki nilai RMSE sebesar 81.80%. dan MAE sebesar 70.79%. 
Perbandingan Kinerja XGBoost dan Random Forest Menggunakan SMOTE pada Klasifikasi Diabetes Multi-Kelas Java Sika Maulana; Safitri Juanita
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10339

Abstract

Type 2 diabetes mellitus is a metabolic disorder that requires an early detection system to support accurate diagnosis identification. One of the major challenges in developing classification models based on clinical medical records is class imbalance, which may cause models to be biased toward the majority class, particularly in multiclass classification involving the Prediabetes class, which represents only 5.3% of the total data. Failure to accurately identify the Prediabetes class may have serious clinical consequences, as this stage still provides an opportunity for early intervention to prevent progression to Diabetes. This study compares the performance of Extreme Gradient Boosting (XGBoost) and Random Forest for multiclass diabetes classification (Normal, Prediabetes, and Diabetes) using clinical data obtained from Medical City Hospital and Al-Kindy Teaching Hospital, Iraq. A Split-First, Resample-Later procedure was employed to prevent data leakage, while Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance the training data, and Grid Search was used for hyperparameter optimization across different train-test split ratios (60:40, 70:30, 80:20, and 90:10). This study provides a comprehensive evaluation of XGBoost and Random Forest on imbalanced multiclass clinical data by comparing their performance before and after SMOTE application across different train–test split ratios using the Split-First, Resample-Later procedure to prevent information leakage between the training and test set. The experimental results demonstrate that Random Forest achieved more stable performance than XGBoost across all evaluation scenarios, both before and after SMOTE application. Both models achieved their best performance with an 80:20 train–test split ratio, whereas SMOTE significantly improved the performance of XGBoost only under the 60:40 split ratio. Furthermore, feature importance analysis identified HbA1c, BMI, and AGE as the most influential clinical attributes for diabetes classification.
Perbandingan SVM dan Logistic Regression untuk Klasifikasi Teks Konsultasi Daring Kehamilan dan Menstruasi Zahra Syifa Prasasti; Safitri Juanita
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35011

Abstract

The high volume of doctors' response texts in online health consultations, particularly on pregnancy and menstruation topics, has increased the need for an accurate and automated medical text classification system. However, these two topics often share similar medical terminology and consultation contexts, making the classification process challenging. This study aims to develop an automatic classification model for Indonesian-language doctors' responses in online health consultations by comparing two algorithms, namely Support Vector Machine (SVM) and Logistic Regression (LR), which were selected because they are widely used in text classification tasks with TF-IDF feature representation and are capable of handling high-dimensional data. The contributions of this study include the development of a classification model using an underexplored Indonesian doctors' response dataset, feature importance analysis to identify dominant medical terms in each category, and a comparative evaluation of SVM and LR performance validated using McNemar's statistical test. The study utilized the public "Doctor's Answer Text Dataset in Indonesian" from Mendeley Data, comprising 17,807 records. The models were developed using the CRISP-DM framework with TF-IDF feature extraction and evaluated across four train-test split ratios using accuracy, precision, recall, and F1-score metrics. The results showed that Logistic Regression (LR) consistently outperformed Support Vector Machine (SVM), achieving the highest accuracy of 0.9359 with an 80:20 train-test split. Feature importance analysis identified "hamil" (pregnant) and "janin" (fetus) as the dominant features for the pregnancy category, and "menstruasi" (menstruation) and "siklus" (cycle) for the menstruation category. These findings demonstrate that TF-IDF-based LR is an effective approach for classifying Indonesian-language online health consultation texts.
Model Deteksi Berita Hoaks Bahasa Indonesia Menggunakan Multinomial Naïve Bayes dan AdaBoost Classifier Haniifaa Hafiizh; Safitri Juanita
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.927

Abstract

The rapid growth of the internet has led to the massive and uncontrolled dissemination of information across various digital platforms, allowing hoax news to reach a wide audience and influence public opinion in a short period of time. This condition highlights the need for a reliable automated detection system. However, existing methods still face limitations in terms of accuracy, result stability, and reliance on manual verification processes. Therefore, this study aims to compare and analyze the performance of two classification algorithms in detecting Indonesian-language hoax news accurately and effectively. This study follows the CRISP-DM framework, beginning with the collection of hoax and non-hoax news articles from turnbackhoax.id and detik.com, resulting in 2,281 samples. The data understanding stage involves analyzing dataset characteristics and evaluating data quality. During data preparation, text elements that explicitly indicate hoax labels are removed, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The dataset is then trained and tested using data split ratios of 70:30, 80:20, and 90:10 by applying Multinomial Naïve Bayes and AdaBoost Classifier algorithms. Model performance is evaluated using a confusion matrix. The results show that AdaBoost achieves superior performance, with an accuracy of 0.9879 (98.79%), outperforming Multinomial Naïve Bayes, which attains an accuracy of 0.9712 (97.12%). The performance of AdaBoost is also consistent across different evaluation scenarios, indicating that it is more suitable as an automated hoax news detection model for the dataset used in this study.
Pemodelan Topik pada Komentar Media Sosial X menggunakan Latent Dirichlet Allocation Ardelia Adzra; Safitri Juanita
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.v6i4.1161

Abstract

Sexual harassment is a social issue widely discussed on the social media platform X. However, the high volume of unstructured comments makes it difficult to manually identify the main topics of discussion. This study aims to identify the main topics in comments related to sexual harassment on X using the Latent Dirichlet Allocation (LDA) method. The data used consist of comments on the topic of sexual harassment collected from X during the 2024–2026 period. The research stages include data collection, data preparation, dictionary and corpus construction, LDA modeling with hyperparameter tuning, evaluation using coherence score, and topic interpretation based on dominant keywords and representative data. The results show that the best LDA model consists of four topics with a coherence score of 0.517. These four topics are interpreted as Handling Cases of Sexual Harassment in Educational Environments, Victims’ Experiences and Psychological Impacts, Cases of Sexual Harassment in Higher Education, and Protection Related to Sexual Harassment. These findings indicate that the LDA method is capable of identifying the main topics in sexual harassment comments and helping to organize unstructured social media data into information that is easier to understand. The contribution of this study is the proposed Latent Dirichlet Allocation (LDA)-based topic modeling approach with hyperparameter tuning to identify and organize unstructured sexual harassment comments on the social media platform X into coherent and interpretable topic clusters. The resulting topic mapping provides valuable insights into the issues that receive the greatest public attention and can serve as a foundation for understanding public concerns. Furthermore, these findings have the potential to support the development of victim support services, including telemedicine-based systems.
Analisis Komparatif Support Vector Regression dan Decision Tree Regression untuk Peramalan Kasus HIV Jawa Barat Risqi Agung Alamsyah; Safitri Juanita
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.33397

Abstract

The increasing number of HIV cases in Indonesia, particularly in West Java, which ranks among the regions with the highest infection rates, highlights the need for forecasting models capable of producing accurate estimates to support health policy planning. This situation underscores the importance of analytical approaches that can capture the dynamic progression of cases over time. This study aims to develop a forecasting model for the number of HIV cases in West Java by applying the CRISP-DM framework and utilizing a dataset categorized by age group for the period 2019–2023. Two regression algorithms, Support Vector Regression (SVR) and Decision Tree Regression (DTR), were compared using three evaluation metrics: Coefficient of Determination (R²), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The findings indicate that DTR delivers superior performance, achieving an R² of 83.62, RMSE of 32.29, and MAPE of 33.30. In contrast, SVR produced an R² of 36.33, RMSE of 84.01, and MAPE of 44.87. Based on these results, Decision Tree Regression is identified as the more effective model for forecasting the number of HIV cases in West Java, providing practical support for health policy decision-making, resource allocation, and prioritization of targeted prevention and intervention programs at the regional level.
Co-Authors Abid Famasya Abdillah Ahya Ulumuddin Ananda Heruansyah Apriantoni Apriantoni Ardelia Adzra Asef Solehuddin Atha Shalih Fatin Azrul Akbar Cornelius Bagus Purnama Putra Devita Diah Prasetyaningrum Diana Permatasari Diana Purwitasari Dolly Virgian Shaka Yudha Sakti Eva Febriyanti Fadil Muhammad Zuhri Farill Andika Wardana Fildzah Fitriani Grace Gata, Grace Haniifaa Hafiizh Ika Disja Afriyani Ita Novita Java Sika Maulana Joko Wijayanto Kurnia Ramadhan Laura Putri Wijaya Handoyo Lukas Ono Harsongko M. Anif Mahmud Didi Nugraha Misbahul Fajri Mochamad Andri Muhammad Anif Muhammad Wildan Muzdalifah Muzdalifah Peter Jaya Putri Hayati Reva Ragam Santika Ripal Abadi Risqi Agung Alamsyah Rizky Indra Ramadhani Samsinar Samsinar Sarita Fabriani Siswanto Siswanto Sopan Bukhari Lubis Syahnaz Sofinar Amru Wahyu Pramusinto Win darto Yuliazmi, Yuliazmi Zahra Syifa Prasasti