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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL TEKNOLOGI DAN OPEN SOURCE Building of Informatics, Technology and Science Jurnal Tekinkom (Teknik Informasi dan Komputer) JISA (Jurnal Informatika dan Sains) Journal of Telenursing (JOTING) Innovation in Research of Informatics (INNOVATICS) Journal of Applied Data Sciences Jurnal Sistem Informasi dan Teknik Informasi DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
Junadhi, Junadhi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Other

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Sentiment Analysis of Sirekap Tweets Using CNN Algorithm Handoko, Handoko; Asrofiq, Ahmad; Junadhi, Junadhi; Negara, Ari Sukma
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.23046

Abstract

Background: The research investigates the application of deep learning models for sentiment analysis on Twitter data related to Indonesia's Sirekap system. Sentiment analysis is crucial for understanding public opinion and enhancing the transparency and reliability of election result recapitulation processes. Objective: The objective of this study is to compare the performance of Convolutional Neural Networks (CNN) and CNN-LSTM models in analyzing sentiments from tweets about the Sirekap system. The study aims to identify the most effective model and preprocessing techniques to improve sentiment classification accuracy. Methods: A comprehensive data preprocessing pipeline was implemented, including cleansing, case folding, tokenizing, normalization, stopword removal, and stemming. To address class imbalance, the SMOTE technique was applied. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Pre-trained word embeddings were used to enhance model performance. Results: The CNN model achieved an accuracy of 85.90%, outperforming the CNN-LSTM model, which achieved 79.91% accuracy. Additionally, the CNN model demonstrated superior precision, recall, and F1-score metrics compared to the CNN-LSTM model. The thorough preprocessing and handling of class imbalance significantly contributed to the enhanced performance of the CNN model. Conclusion: The research emphasizes the effectiveness of deep learning approaches, particularly CNNs, in sentiment analysis tasks. The findings highlight the importance of comprehensive preprocessing and class imbalance handling. The use of pre-trained word embeddings and various evaluation metrics ensures robust model performance. These insights contribute to improving the accuracy and efficiency of sentiment classification, thereby enhancing the reliability and transparency of election result recapitulation processes.
Penerapan Fitur Firebase Cloud Messaging Pada Sistem Kontrol Pembayaran Iuran BPJS Ketenagakerjaan Berbasis Mobile Meidina, Mita; Junadhi, Junadhi; Rio, Unang; Efendi, Yoyon
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (954.611 KB) | DOI: 10.47065/bits.v4i1.1603

Abstract

BPJS of Employment is the organizer of a social security program that functions to protect workers through four employment social security programs, namely Work Accident Insurance, Old Age Security, Death Security, and Pension Security. Information regarding the payment of BPJS Employment Contributions is an important factor in supporting social security programs for workers and employees. This is because there are still many participants who are in arrears in payment of the BPJS Employment Contribution and there is no immediate information regarding the payment of the BPJS Employment Contribution. In this study, an application was made so that an administrator could send notification messages regarding the payment of the BPJS Employment Monthly Contribution to participants and could receive reports on the payment of contributions that have been made by participants. The system design this time was assisted by Firebase Cloud Messaging (FCM) technology which functions as a web service. Messages that have been sent can be stored in a database that can be viewed on the admin web page. The results of this study are successful in building a BPJS employment payment control application by implementing the mobile-based Firebase Cloud Messaging (FCM) feature. This application can send notifications to participants regarding due information on dues payments in real time. Makes it easier for participants to get the latest information quickly. For the tests carried out using the functionality testing of the system by performing simulations on each function of the system made and concluded that the system functions can run well
Efektifitas Penggunaan Sistem Mentoring Dalam Meningkatkan Kualitas Perawat Di Rumah Sakit Nita, Yureya; Devita, Yeni; Kristanti, Martina Sinta; Junadhi, Junadhi; Puswati, Desti
Journal of Telenursing (JOTING) Vol 6 No 2 (2024): Journal of Telenursing (JOTING)
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/joting.v6i2.12647

Abstract

This study aims to determine the effectiveness of the use of the Mentoring system in helping nurses to improve the quality of nurses in hospitals. The method used is analytical survey research using a cross sectional approach. The results of the study showed that the effectiveness of the mentoring system (82.5%), satisfaction with the mentoring system (76.6%), the usefulness in improving the Competence of Nurses (77.5%), and the technology and accessibility of the mentoring system (70%). It was concluded that the Mentoring System was more than 76% effective in improving the quality of nurses in hospitals. Keywords: Nurse Quality, Mentoring, Mentor
Leveraging K-Nearest Neighbors with SMOTE and Boosting Techniques for Data Imbalance and Accuracy Improvement Lubis, Adyanata; Irawan, Yuda; Junadhi, Junadhi; Defit, Sarjon
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.343

Abstract

This research addresses the issue of low accuracy in sentiment analysis on Israeli products on social media, initially achieving only 64% using the K-NN algorithm. Given the ongoing Israeli-Palestinian conflict, which has garnered widespread international attention and strong opinions, understanding public sentiment towards Israeli products is crucial. To improve accuracy, the study employs SMOTE to handle data imbalance and combines K-NN with boosting algorithms like AdaBoost and XGBoost, which were selected for their effectiveness in improving model performance on imbalanced and complex datasets. AdaBoost was chosen for its ability to enhance model accuracy by focusing on misclassified instances, while XGBoost was selected for its efficiency and robustness in handling large datasets with multiple features. The research process includes data pre-processing (cleaning, normalization, tokenization, stopwords removal, and stemming), labeling using a Lexicon-Based approach, and feature extraction with CountVectorizer and TF-IDF. SMOTE was applied to oversample the minority class to match the number of instances in the majority class, ensuring balanced representation before model training. A total of 1,145 datasets were divided into training and testing data with a ratio of 70:30. Results demonstrate that SMOTE increased K-NN accuracy to 77%. Interestingly, combining K-NN with AdaBoost after SMOTE achieved 72% accuracy, which, although lower than the 77% achieved with SMOTE alone, was higher than the 68% accuracy without SMOTE. This discrepancy can be attributed to the added complexity introduced by AdaBoost, which may not synergize as effectively with SMOTE as XGBoost does, particularly in this dataset's context. In contrast, K-NN with XGBoost after SMOTE reached the highest accuracy of 88%, demonstrating a more effective combination. Boosting without SMOTE resulted in lower accuracies: 68% for KNN+AdaBoost and 64% for KNN+XGBoost. The combination of K-NN with SMOTE and XGBoost significantly improves model accuracy and reliability for sentiment analysis on social media.
Sentiment Analysis of Societal Attitudes Toward the Childfree Lifestyle Using Latent Dirichlet Allocation and Support Vector Machines Husen, Ratna Andini; Agustin, Agustin; Erlinda, Susi; Junadhi, Junadhi; Perumal, Thinagaran
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.12005

Abstract

This research investigates societal perspectives on the childfree lifestyle through Intent Sentiment Analysis, combining Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) techniques. The childfree lifestyle, a deliberate decision by individuals or couples to remain childless, has spurred extensive public discourse, particularly on platforms like Twitter. This research aims to analyze sentiments and intentions within these discussions to uncover their implications for social dynamics and familial relationships. Using LDA, dominant topics were identified from a dataset of Twitter comments on the childfree topic. LDA uncovered hidden themes by modeling topics as mixtures of words, which were subsequently classified into positive, negative, and neutral sentiments using SVM. Data preprocessing included cleaning, tokenization, and stop word removal, while oversampling with SMOTE addressed class imbalances. The optimal number of topics was determined using coherence scores, with the highest coherence value of 0.400 achieved at one topic. The findings revealed that positive sentiments were classified more effectively than negative and neutral sentiments when using LDA and SVM with SMOTE. The top 10 topics primarily reflected societal commentary on the childfree lifestyle. Challenges included incomplete preprocessing, suboptimal clustering of similar themes, and imbalanced data, which limited the effectiveness of topic modeling and classification. Addressing these issues through improved feature selection, parameter optimization, and data augmentation could enhance performance for underrepresented categories. This research provides valuable insights into public attitudes toward the childfree lifestyle, offering implications for social research and policy development in the context of evolving societal norms.  
Implementasi Metode User Centered Design Pada Aplikasi Donasi Pakaian Bekas Berbasis Web Fadliellah, Mardiyat; Junadhi, Junadhi; Susi Erlinda; Herwin
Jurnal SANTI - Sistem Informasi dan Teknik Informasi Vol. 5 No. 1 (2025)
Publisher : Yayasan Rahmatan Fiddunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/santi.v5i1.929

Abstract

Penelitian ini bertujuan untuk mengembangkan aplikasi donasi pakaian bekas berbasis web dengan menggunakan metode User Centered Design (UCD). UCD adalah pendekatan desain yang berfokus pada kebutuhan dan preferensi pengguna akhir selama seluruh proses pengembangan aplikasi. Melalui tahapan UCD, mulai dari analisis kebutuhan pengguna, perancangan, prototyping, hingga pengujian, aplikasi ini diharapkan mampu memberikan pengalaman pengguna yang optimal dan memudahkan proses donasi pakaian bekas. Dalam penelitian ini, proses pengembangan aplikasi melibatkan partisipasi aktif dari pengguna potensial untuk memastikan aplikasi memenuhi kebutuhan dan harapan mereka. Setelah tahap pengembangan selesai, aplikasi diuji menggunakan metode blackbox testing untuk memastikan fungsionalitasnya. Blackbox testing dilakukan dengan cara menguji aplikasi berdasarkan input dan output yang dihasilkan tanpa memperhatikan struktur internal atau kode program. Hasil penelitian menunjukkan bahwa implementasi metode UCD pada aplikasi donasi pakaian bekas berbasis web dapat meningkatkan kenyamanan dan kepuasan pengguna. Pengujian dengan blackbox testing menunjukkan bahwa semua fitur aplikasi berfungsi dengan baik sesuai dengan spesifikasi yang diharapkan. Aplikasi ini diharapkan dapat menjadi solusi yang efektif dalam memfasilitasi donasi pakaian bekas secara online, sehingga dapat membantu masyarakat yang membutuhkan.
Improving Evaluation Metrics for Text Summarization: A Comparative Study and Proposal of a Novel Metric Junadhi, Junadhi; Agustin, Agustin; Efrizoni, Lusiana; Okmayura, Finanta; Habibie, Dedi Rahman; Muslim, Muslim
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.547

Abstract

This research evaluates and compares the effectiveness of various evaluation metrics in text summarization, focusing on the development of a new metric that holistically measures summary quality. Commonly used metrics, including ROUGE, BLEU, METEOR, and BERTScore, were tested on three datasets: CNN/DailyMail, XSum, and PubMed. The analysis revealed that while ROUGE achieved an average score of 0.65, it struggled to capture semantic nuances, particularly for abstractive summarization models. In contrast, BERTScore, which incorporates semantic representation, performed better with an average score of 0.75. To address these limitations, we developed the Proposed Metric, which combines semantic similarity, n-gram overlap, and sentence fluency. The Proposed Metric achieved an average score of 0.78 across datasets, surpassing conventional metrics by providing more accurate assessments of summary quality. This research contributes a novel approach to text summarization evaluation by integrating semantic and structural aspects into a single metric. The findings highlight the Proposed Metric's ability to capture contextual coherence and semantic alignment, making it suitable for real-world applications such as news summarization and medical research. These results emphasize the importance of developing holistic metrics for better evaluation of text summarization models.
Analisis User Experience Sistem Tracer Alumni USTI dengan Metode SUS sebagai Upaya Peningkatan Kualitas Penggunaan Putra, Jean Riko Kurniawan; Junadhi, Junadhi; Agustin, Agustin; Herwin, Herwin; Zoromi, Fransiskus; Andesa, Khusaeri
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 1: JUNI 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i1.6709

Abstract

Penelitian ini bertujuan untuk menganalisis pengalaman pengguna (User Experience/UX) pada Sistem Tracer Alumni Universitas Sains dan Teknologi Indonesia (USTI) menggunakan metode System Usability Scale (SUS). Sistem Tracer Alumni merupakan platform penting dalam memantau perkembangan karier lulusan dan evaluasi mutu pendidikan. Evaluasi UX menjadi aspek krusial untuk memastikan sistem mudah digunakan dan memuaskan pengguna. Data dikumpulkan melalui survei SUS dari 40 responden yang merupakan pengguna aktif sistem. Hasil analisis menunjukkan skor rata-rata SUS sebesar 69,30, yang mengindikasikan tingkat kegunaan sistem tergolong baik. Meskipun demikian, terdapat kebutuhan peningkatan pada aspek kemudahan penggunaan dan pembelajaran untuk meningkatkan kenyamanan pengguna. Variasi skor mengindikasikan perlunya perbaikan desain antarmuka dan fitur panduan agar sesuai dengan kebutuhan beragam pengguna. Temuan ini memberikan gambaran empiris mengenai UX sistem dan menjadi dasar rekomendasi pengembangan agar Sistem Tracer Alumni USTI lebih user-friendly dan efektif. Implementasi rekomendasi diharapkan dapat meningkatkan partisipasi pengguna dan mendukung pengelolaan data alumni secara optimal.
ANALISIS SENTIMEN MASYARAKAT INDONESIA TERHADAP PELUNCURAN DANANTARA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Sari, Retno; Agustin, Agustin; Rahmiati, Rahmiati; Junadhi, Junadhi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2351

Abstract

As one of the latest innovations in Indonesia, the launch of Danantara has attracted public attention, especially the X (formerly Twitter) social media community. Understanding public sentiment about the launch is crucial due to the varied reactions. Using the Support Vector Machine (SVM) method as the primary classification algorithm, this study aims to examine how Indonesians perceive the launch of Danantara. Data collected through scraping techniques from social media posts were then processed through text preprocessing processes such as data cleaning, tokenization, and normalization. Categorizing sentiment into positive, negative, or neutral can be done using the signal variable model (SVM). The results show that the majority of the public has a certain sentiment towards the launch of Danantara, as the SVM model can classify sentiment very accurately. In the future, this study will help stakeholders understand public opinion and create better communication plans.
Adaptive Neural Collaborative Filtering with Textual Review Integration for Enhanced User Experience in Digital Platforms Efrizoni, Lusiana; Ali, Edwar; Asnal, Hadi; Junadhi, Junadhi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.944

Abstract

This research proposes a hybrid rating prediction model that integrates Neural Collaborative Filtering (NCF), Long Short-Term Memory (LSTM), and semantic analysis through Natural Language Processing (NLP) to enhance recommendation accuracy. The main objective is to improve alignment between system predictions and actual user preferences by leveraging multi-source information from the Amazon Movies and TV dataset, which includes explicit user–item ratings and textual reviews. The core idea is to combine three complementary processing paths—(1) user–item interaction modeling via NCF, (2) temporal dynamics capture through LSTM, and (3) semantic understanding of reviews using NLP—into a unified deep learning-based adaptive architecture. Experimental evaluation demonstrates that this multi-input approach outperforms the baseline collaborative filtering model, with the Mean Absolute Error (MAE) reduced from 1.3201 to 1.2817 (a 2.91% improvement) and the Mean Squared Error (MSE) reduced from 2.2315 to 2.1894 (a 1.89% improvement). Training metrics visualization further shows a stable convergence pattern, with the MAE gap between training and validation consistently below 0.03, indicating minimal overfitting. The findings confirm that integrating cross-dimensional signals significantly enhances predictive performance and can contribute to increased user satisfaction and engagement in recommendation platforms. The novelty of this work lies in the simultaneous integration of interaction, temporal, and semantic dimensions into a single adaptive recommendation framework, a configuration not jointly explored in prior studies. Moreover, the flexible architecture enables adaptation to other domains such as e-commerce, music, or online learning, broadening its practical applicability.
Co-Authors Adyanata Lubis Agustin Agustin Anam, M. Khairul Andesa, Khusaeri Andhika, Rivaldi Dwi Asrofiq, Ahmad Desti Puswati Devita, Yeni Edwar Ali Efrizoni, Lusiana Erlinda, Susi Fadliellah, Mardiyat Fathur Rahman Fauzan, Bias Arbi Finanta Okmayura Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Handoko Handoko Helda Yenni, Helda Herwin Herwin Herwin, Herwin Husen, Ratna Andini Irfansyah Irfansyah Jamaris, M Kudadiri, Parlindungan Martina Sinta Kristanti Meidina, Mita Millenio, Benino Giordiola Muharnisa Haspin, Wafiq Muslim Muslim Negara, Ari Sukma Nita, Yureya Nu'man, Nu'man Perumal, Thinagaran Putra, Jean Riko Kurniawan Putri, Silvyana Dwi Rahmiati Rahmiati Retno Sari Sarjon Defit Surya, Heru Satria Susanti Susanti SUSI ERLINDA Triyani Arita Fitri Unang Rio Yoyon Efendi Yuda Irawan