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All Journal Jurnal Fitopatologi Indonesia Jurnal Agroteknos Sistemasi: Jurnal Sistem Informasi Al Ishlah Jurnal Pendidikan Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Pengabdian Pada Masyarakat INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Teknik Informatika (JUTIF) Community Empowerment Journal of Electrical Engineering and Computer (JEECOM) Journal Of Human And Education (JAHE) J-COSCIS : Journal of Computer Science Community Service CONSEN: Indonesian Journal of Community Services and Engagement Jurnal Teknologi Komputer dan Informasi Contributions of Central Research Institute for Agriculture GMPI Conference Series JURNAL KARYA ILMIAH MULTIDISIPLIN INOVTEK Polbeng - Seri Informatika IndoAI: Journal of Artificial Intelligence and Computational Logic Journal of Community Outreach in Science & Society
Lisnawita Lisnawita
Fakultas Ilmu Komputer, Universitas Lancang Kuning
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Importance Performance Analysis (IPA) of Patient Satisfaction with Fuzzy Logic at the Rumbai Maternity Clinic Costaner, Loneli; Lisnawita, Lisnawita; Guntoro, Guntoro
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3527

Abstract

This study focuses on the level of patient satisfaction at the Rumbai Maternity Clinic. Quality of service is the main key that influences patient trust and satisfaction with this health facility. Therefore, the purpose of this study was to analyze patient satisfaction with the Importance Performance Analysis (IPA) method using fuzzy logic. This study will identify service attributes that are considered important by patients and evaluate the extent to which patient expectations have been met at the clinic. Attributes studied include cleanliness of facilities, courtesy of medical staff, availability of medicines, quality of medical services, information conveyed to patients, and others. The (IPA) method will help measure the level of importance and performance of each service attribute based on patient perceptions. Fuzzy logic is used to overcome the complexity and subjectivity of assessing patient satisfaction. The results of this study are expected to provide a comprehensive picture of patient perceptions and satisfaction at the Rumbai Maternity Clinic. Clinical management can use the results of this study to identify service improvement priorities and increase patient satisfaction. The scientific contribution of this research lies in combining the IPA method and fuzzy logic in the analysis of patient satisfaction. Thus, this research has the potential to help improve the quality of health services at the Rumbai Maternity Clinic and can be applied as a guide for developing similar methods in other health facilities.
Feature Extraction Analysis for Diabetic Retinopathy Detection Using Machine Learning Techniques Costaner, Loneli; Lisnawita, Lisnawita; Guntoro, Guntoro; Abdullah, Abdullah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4600

Abstract

Diabetic retinopathy is a serious complication of diabetes that can lead to blindness if not detected and treated early. Automated detection of diabetic retinopathy requires effective feature extraction techniques to enhance diagnostic accuracy. This study aims to develop a method for detecting diabetic retinopathy by utilizing Local Binary Pattern (LBP) combined with wavelet transform, and then classifying the extracted features using Support Vector Machine (SVM). The approach includes feature extraction from retinal images using LBP and wavelet transform. The extracted features are subsequently classified with SVM to evaluate performance in detecting diabetic retinopathy. Analysis results show that the dominant feature is found in the fifth row with a value of 0.57006, indicating the effectiveness of the LBP method in feature extraction. The developed model demonstrates high performance with an accuracy of 95.59%, precision of 96%, recall of 97.96%, and F1-score of 96.97%. The combination of feature extraction methods with SVM proves to be effective and reliable in detecting diabetic retinopathy, offering low error rates and high accuracy, thus potentially serving as a valuable tool in clinical diagnosis
Mendeley-Assisted PTK Writing Assistance for Teachers of SMKN 1 Mempura Siak: Pendampingan Penulisan PTK Berbantuan Mendeley Untuk Guru SMKN 1 Mempura Siak Lisnawita, Lisnawita; Asril , Elvira; Muzdalifah , Indah
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 5 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i5.16417

Abstract

Teachers are expected to be able to articulate creative ideas and research findings arising from diverse difficulties encountered in the field toimprove the quality of learning both within and outside the classroom. This document serves as a reference andunique requirementfor teachers managing promotions. However, many teachers struggle with writing scientific papers because one of the conditions for their promotion is to produce scientific papers that will be published in a journal. The purpose of this paper is to provide answers for teachers who are makingscientific papers. The Mendeley desktop application can help teachers compile references and bibliographies in article papers. This activitywas conductedat SMKN 1 Mempura. This activity is carried out through the use of theory and practice. The outcome of this activity was that the teacher responded positively, gained new knowledge about the Mendeley program, and earnedbenefits in writing PTK.It is evident from one of the teachers who was able to publish his scientific work in the Dinamisia Journal after participating in this PKM activity
Improving Digital Literacy to Prevent the Spread of Hoax News: Peningkatan Literasi Digital untuk Mencegah Penyebaran Berita Hoax Lisnawita, Lisnawita; Guntoro, Guntoro; Anggie Johar, Olivia; Costaner, Loneli
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 1 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i1.17275

Abstract

Perkembangan teknologi, khususnya internet, membawa dampak signifikan dengan memungkinkan penyebaran berita hoaks. Literasi digital menjadi penting, terutama bagi siswa aktif dalam internet dan media sosial. Penelitian ini menggunakan metode ceramah, kuesioner, dan penjelasan untuk meningkatkan literasi digital siswa dalam mengenali dan mencegah penyebaran berita palsu. Studi literatur menyoroti dampak negatif berita hoaks, terutama terkait isu SARA dan politik, di media sosial seperti Twitter dan Instagram. Masalah utama literasi digital di sekolah adalah kurangnya pengetahuan tentang program literasi digital. Pengabdian ini bertujuan memberikan landasan untuk meningkatkan kesadaran siswa terhadap penyebaran berita hoaks di era digital. Hasil evaluasi menunjukkan peningkatan pengetahuan peserta sebesar 69.78%, membuktikan bahwa pendekatan interaktif dan penerapan literasi digital dapat memberikan manfaat positif.
Exploring Research and Service Information System Usability by Heuristic Evaluation as a Compelement of System Usability Scale Guntoro; Lisnawita; Loneli Costaner
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.5571

Abstract

The Research and Community Service Information System of LPPM University Lancang Kuning is a web-based application designed to facilitate research and community service activities at the university. This study incorporated two methodologies: a descriptive approach with qualitative analysis for heuristic data collection, and the System Usability Scale (SUS) method, employing quantitative analysis. The research process included stages of problem analysis, literature review, data collection, data analysis, and formulating recommendations based on the findings and discussions. The heuristic evaluation, the first method applied, revealed that aspects H1, H3, and H4 scored 1 when rounded, indicating these were merely cosmetic issues not requiring immediate attention unless spare time was available. Conversely, aspects H2, H5, H6, H7, H8, H9, and H10 scored 2 when rounded, categorizing them as minor usability issues needing resolution, albeit with low priority, to prevent potential user difficulties. Recommendations for these seven heuristic aspects scoring 2 encompassed improvements in system information clarity, feedback processes, image utilization, color selection, grammar quality, and writing consistency. The second method, the SUS, indicated that most users demonstrated adequate skills in terms of learnability, efficiency, memorability, error management, and overall satisfaction with their system usage experience.
Penerapan Optimasi Gradient Boosting dalam Prediksi Nilai Transaksi Pelanggan di E-CRM Fluffy Cat Shop Tiara; Lisnawita; Lucky Lhaura Van FC
IndoAI: Journal of Artificial Intelligence and Computational Logic Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic
Publisher : IndoAI: Journal of Artificial Intelligence and Computational Logic

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of e-commerce encourages companies to optimally leverage customer data through an Electronic Customer Relationship Management (E-CRM) system. Fluffy Cat Shop, an online store for cat supplies, faces challenges in accurately predicting customer transaction value. This study aims to optimize the Gradient Boosting algorithm for predicting customer transaction value within the E-CRM system of Fluffy Cat Shop. The research methods include collecting customer transaction data, data preprocessing (cleaning, encoding, and normalization), building a Gradient Boosting model, and optimizing hyperparameters using the Grid Search method. Model evaluation is conducted using the MAE, RMSE, and R² Score metrics. The results show that after optimization, the model’s performance improves with an R² Score of 0.8, indicating that the model can explain 80% of the variation in customer transaction value. The error values also decrease compared to the initial model.
Complex Word Identification in Indonesian Children’s Texts: An IndoBERT Baseline and Error Analysis Lisnawita, Lisnawita; Bakar, Juhaida Abu; Rasli, Ruziana Mohamad; Costaner, Loneli; Guntoro, Guntoro
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5501

Abstract

Complex Word Identification (CWI) is a crucial step for building text simplification systems, especially for Indonesian children’s reading materials where unfamiliar vocabulary can hinder comprehension. This study formulates token-level CWI for Indonesian children’s texts and establishes two baselines:  an interpretable rule-based model using linguistic features e.g., length, syllable heuristics, and affix patterns, and an IndoBERT model fine-tuned for token classification. This study construct and annotate a children’s text corpus and evaluate both approaches using standard classification metrics. On the test set (22.584 tokens), IndoBERT achieves an F1-score of 0.9972 for the CWI class, substantially outperforming the rule-based baseline (F1 = 0.8607). The IndoBERT system makes only 39 errors (23 false positives and 16 false negatives), indicating near-perfect performance under the evaluated setting. Furthermore, this study provides an error analysis to highlight remaining failure patterns and borderline cases that are difficult even for contextual models. The resulting benchmark and findings contribute to Informatics/Computer Science by providing a strong baseline and analysis for educational NLP in a low-resource language setting, supporting the development of Indonesian child-oriented NLP resources and downstream text simplification tools.
Empowering Vocational School Students Through Digital Security Training to Prevent Cyber Threats: A Case Study at SMKN 7 Pekanbaru : Pemberdayaan Siswa SMK Melalui Pelatihan Keamanan Digital untuk Mencegah Ancaman Siber: Studi Kasus di SMKN 7 Pekanbaru Guntoro Guntoro; Lisnawita Lisnawita; Winda Monika; Loneli Costaner
CONSEN: Indonesian Journal of Community Services and Engagement Vol. 6 No. 1 (2026): Consen: Indonesian Journal of Community Services and Engagement
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/consen.v6i1.2326

Abstract

Digital devices now form the backbone of nearly every classroom, yet that convenience comes tangled with new cybersecurity peril. Students in vocational tracks sit at the crossroads: they click through learning modules all day but rarely receive targeted instruction on how to keep themselves safe online. Without that practical know-how, the hallways of a single school can quietly accumulate risks like data leaks, identity theft, and rogue software. In response, the present study piloted a campus-based workshop designed to meet learners exactly where they are. Courses were delivered at SMKN 7 Pekanbaru, involving thirty trade students who volunteered despite their busy schedules. Lectures spoke in plain language; hands-on exercises replayed incidents pulled from local news; quick-fire quizzes and spirited group debates stitched it all together. Student mastery was quantified by side-by-side snapshots taken before and after the event, measured against five essential security benchmarks. The opening average sat at a modest 18.7 out of 25; the closing number soared to 24.4. A paired t-test for the twenty-nine complete sets of data returned t(29) = 13.25, p < 0.0001, clearly ruling out chance. Glance at the run charts and the upward drift is obvious: every learner moved forward, and the room buzzed with confidence that had been absent hours earlier. Recent research confirms that focused, brief cybersecurity workshops can significantly boost learners grasp of online threats and the defensive habits they employ. Because the instructional framework proved practical, other institutions are well-positioned to adopt it and thereby reduce the cyber vulnerabilities that affect campus communities.
Optimization of LBP Texture Feature Extraction using Correlation And Mi For SVM-Based Diabetic Retinopathy Classification loneli costaner; lisnawita lisnawita; Guntoro Guntoro
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/4vrj4930

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness, making early detection based on retinal fundus images crucial. This study proposes a DR classification method with a primary contribution in feature optimization: integrating the LBP Contrast feature with a Local Binary Pattern (LBP) histogram and performing hybrid feature selection based on Mutual Information (MI) to assess relevance and correlation analysis to reduce redundancy. This method was tested using 168 images from the public Messidor dataset, with 100 images for training and 68 for testing to evaluate performance. Classification was performed using a Support Vector Machine (SVM) with a linear kernel, where model performance was evaluated before and after optimization to measure the significance of the improvement. The results showed a significant improvement after optimization, with accuracy increasing from 88% to 94%, recall increasing from 88% to 100%, and F1-score increasing from 0.92 to 0.96. Although precision decreased slightly from 96% to 93%, increasing recall to 100% is considered more crucial in a medical context as it minimizes the risk of missed positive cases. These findings confirm that the proposed feature optimization approach can significantly improve the accuracy and reliability of the DR detection system, offering potential clinical relevance for supporting early intervention.
Optimizing Random Forest for IoT Cyberattack Detection using SMOTE: A Study on CIC-IoT2023 Dataset Guntoro Guntoro; Lisnawita Lisnawita; Loneli Costaner
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5382

Abstract

The growing number of Internet of Things devices has led to an increased risk of complex and diverse cyberattacks. However, a significant challenge in this domain is the imbalanced class distribution in most Internet of Things datasets, cautilizing classification algorithms to be biased towards the majority class, hindering effective threat detection. This study addresses this issue by leveraging the Random Forest algorithm optimised by the Synthetic Minority Oversampling Technique. This research aims to develop an effective model for detecting cyberattacks in Internet of Things environments by resolving class imbalance issues inside of the CIC-IoT2023 dataset. The methodology involves several stages, comprising data preprocessing and applying Synthetic Minority Oversampling Technique for data balancing. The balanced dataset was then used to train a Random Forest model, by its performance evaluated utilizing accuracy, precision, recall, F1-score, and Cohen's Kappa metrics. The results demonstrate the model's effectiveness, achieving an accuracy of 99.01%, an F1-score of 98.96%, and a Cohen's Kappa of 98.92%. This marks a notable improvement in performance, particularly in detecting minority classes, compared to the model trained devoid of Synthetic Minority Oversampling Technique, that struggled to identify several less common attack types. The outcomes suggest that combining Random Forest by Synthetic Minority Oversampling Technique can significantly enhance the development of intrusion detection systems by improving detection accuracy for all 33 attack types and reducing the risks associated by undetected threats. In conclusion, this study advances Internet of Things cybersecurity by presenting an effective and efficient method for addressing data imbalance in attack detection. Future research should focus on evaluating the model's robustness utilizing more complex datasets and enhancing its performance for real-time deployment on resource-constrained Internet of Things Devices.
Co-Authors Abdullah Abdullah Ahmad Rafiqi Tantawi Ahmad Zamsuri, Ahmad Al-ghifari, Faza Anto Ariyanto Antonius Fernando Asma Alhusna Bakar, Juhaida Abu Bambang HADISUTRISNO Budia Misri Costaner, Loneli Costaner Dafwen Toresa David Setiawan Desmawan, Wahyu Edriyansyah Elvira Asril, Elvira Evi Lianda Fajrizal Fajrizal, Fajrizal Fana Wiza, Fana Febi Yanto Fenty Widya Fuad Renadi GEDE SUASTIKA Guntoro Guntoro, Guntoro Hamidah Hanum Hartana, I Haryono Semangun Ikhsan Hidayat Irda Safni Jeni Wardi Johar, Olivia Anggie Keumala Anggraini Khairunnisa Lubis, Khairunnisa Latifa Siswati loneli costaner Loneli Costaner Lucky Lhaura Van FC Lucky Lhaura Van FC, Lucky Lhaura Mariza Devega Monika, Winda Monika Muhamad Sadar, Muhamad Musfawati Musfawati Musfawati Musfawati Musfawati Musfawati Mustika Mustika Muzdalifah , Indah Muzdalifah, Indah NABABAN, TRIWANTO Novisa Rahmadani novi Nugraha, Alif Nurhamin Nurhamin Pandu Pratama Putra, Pandu Pratama Panigoran, Abdullah Rasli, Ruziana Mohamad Rezmia Febrina Ririn Sari Wati Rizky Octa Putri Charin Rometdo Muzawi RR. Ella Evrita Hestiandari Saragih, Wismaroh Sanniwati Sitepu, Suzanna Fitriani Supramana Susi Handayani Sutejo Sutejo Syafrika Deni Rizki Tantawi, A. Rafiqi Tiara Toresa, Dafwen Wenni Syafitri Yogi Yunefri, Yogi