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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Infotekmesin Journal of Information Systems and Informatics Scientific Journal of Informatics Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Technology and Informatics (JoTI) TIERS Information Technology Journal Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM)
Ali Nur Ikhsan
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PELATIHAN PEMBUATAN VIDEO PEMBELAJARAN DENGAN MENGGUNAKAN MEDIA CANVA DI SMK NEGERI KEBASEN Ali Nur Ikhsan; Alif Nur Fadilah; Imun Faizal
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 7, No 2 (2023): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v7i2.15211

Abstract

 ABSTRAKSMK Negeri Kebasen merupakan salah satu sekolah menengah kejuruan yang berada di Kabupaten Banyumas. SMK Negeri Kebasen selalu meningkatkan kualitas anak didik dan berupaya menjadi sekolah unggulan dalam bidang teknologi dan rekayasa di Kabupaten Banyumas. Dalam menjalankan aktivitas pembelajaran para guru berupaya untuk membuat kondisi belajar yang interaktif dan menyenangkan salah satunya dengan menggunakan video pembelajaran. Dalam pembuatan video pembelajaran masih banyak guru yang mengalami kesulitan dan memutuskan untuk memberikan materi secara konvensional. Tim pengabdian masyarakat Amikom Mitra Masyarakat (AMM) berinisiatif melakukan pelatihan Pembuatan Video Pembelajaran Dengan Menggunakan Media Canva di SMK Negeri Kebasen. Metode yang digunakan dalam pelatihan ini yaitu berupa workshop. Tim PkM melakukan pelatihan secara langsung dengan 1 narasumber sebagai pemateri workshop dan 2 pendamping untuk mendampingi guru dalam pelaksanaan workshop. Pelatihan ini dapat menambah pengetahuan guru dalam memanfaatkan Canva sebagai media pembuatan video pembelajaran. Kata kunci: pelatihan; pengabdian masyarakat; video pembelajaran; canva; workshop. ABSTRACTKebasen State Vocational School is one of the vocational high schools in Banyumas Regency. Kebasen State Vocational School always improves the quality of students and strives to become a superior school in the field of technology and engineering in Banyumas Regency. In carrying out learning activities, teachers strive to create interactive and fun learning conditions, one of which is by using learning videos. In making learning videos there are still many teachers who experience difficulties and decide to provide material conventionally. The Amikom Mitra Masyarakat (AMM) community service team took the initiative to conduct training on Making Learning Videos Using Canva Media at the Kebasen State Vocational School. The method used in this training is in the form of a workshop. The PkM team conducts hands-on training with 1 resource person as a workshop speaker and 2 assistants to assist the teacher in conducting the workshop. This training can increase teachers' knowledge of using Canva as a medium for making learning videos. Keywords: training; community service; tutorial video; canva; workshop.
Penerapan Multi-Palette Color untuk Pemberian Saran Pemilihan Warna Tema Desain Visual Vektor Suliswaningsih; Adam Prayogo Kuncoro; Ali Nur Ikhsan; Muhammad Thoriq Jamil; Syahrul Sani
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.2081

Abstract

In graphic design, many creative applications offer many templates. This design platform is suitable for creative designers and hobbyists such as marketers, bloggers, social media managers, etc. In a design workflow, users select a template and replace elements with their resources. Instead of creating one color palette for all elements, researchers extract multiple color palettes from each visual element in a graphic document and then combine them into a set of colors. Researchers design sample color schemes to complement color sets and we recommend colors that might be determined based on the color context in a multi-palette. Researchers conducted model training and created a color recommendation system for a collection of vector visual designs. The proposed color recommendation method is targeted to be a color prediction medium, as well as a color recommendation system on vector media. The results of this study are in the form of color recommendations for vector graphic design based on a multi-palette of visual elements.
Digitalisasi buku ajar melalui pelatihan menulis buku ajar dengan pemanfaatan teknologi untuk pembelajaran di perguruan tinggi Dani Arifudin; M. Syaiful Amin; Deuis Nur Astrida; Ali Nur Ikhsan
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 3 (2025): May
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i3.31204

Abstract

AbstrakProgram pengabdian ini bertujuan untuk meningkatkan efektivitas pelatihan penulisan buku ajar bagi dosen di perguruan tinggi Purwokerto melalui digitalisasi dan pemanfaatan teknologi. Mitra dalam program ini adalah Zahira Media Publisher, yang telah beberapa kali mengadakan pelatihan, namun masih menghadapi kendala rendahnya tingkat penyelesaian dan penerbitan buku ajar oleh peserta. Solusi yang ditawarkan mencakup penerapan teknologi dalam penulisan, digitalisasi, dan distribusi buku ajar. Dosen akan diberikan pelatihan dalam penggunaan perangkat lunak pengolah kata, manajemen referensi, desain tata letak, serta publikasi digital melalui platform seperti Google Play Books dan repository universitas. Selain itu, sistem pendampingan online dan integrasi dengan Learning Management System (LMS) akan diterapkan untuk memastikan keberlanjutan dan efektivitas program. Kegiatan pelatihan ini diikuti oleh 30 dosen, dengan hasil 3 naskah buku ajar berhasil diterbitkan dalam bentuk digital maupun cetak, dan 25 naskah lainnya telah mencapai tahap akhir penyelesaian. Evaluasi menunjukkan peningkatan signifikan dalam keterampilan menulis akademik dan literasi digital peserta. Program ini terbukti efektif dalam mendukung penguatan bahan ajar di perguruan tinggi serta memperluas akses mahasiswa terhadap buku ajar berkualitas dalam format digital. Kata kunci:. buku ajar; pelatihan menulis; teknologi pendidikan; penerbitan buku. AbstractThis community service program aims to improve the effectiveness of open book writing training for lecturers at Purwokerto universities through digitalization and utilization of technology. The partner in this program is Zahira Media Publisher, which has held several trainings, but still faces the problem of low levels of completion and publication of open books by participants. The solutions offered include the application of technology in writing, digitizing, and distributing open books. Lecturers will be given training on the use of word processing software, reference management, layout design, and digital publication through platforms such as Google Play Books and university repositories. In addition, an online mentoring system and integration with the Learning Management System (LMS) will be implemented to ensure the continuity and effectiveness of the program. This training activity was attended by 30 lecturers, with the results of 3 textbook manuscripts being successfully published in digital and printed form, and 25 other manuscripts having reached the final stage of completion. The evaluation showed a significant increase in the participants' academic writing skills and digital literacy. This program has proven effective in supporting the strengthening of teaching materials in universities and expanding student access to quality textbooks in digital format. Keywords: textbooks; writing training; educational technology; book publishing.
Event-Based Detection of Provocative Political Discourse on Indonesian Twitter: A Comparative Study of SVM and IndoBERT Evril Fadrekha Cahyani; Ali Nur Ikhsan; Deuis Nur Astrida
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1409

Abstract

Political polarization on Indonesian social media intensified during the August 2025 House of Representatives (DPR) demonstrations, where provocative and sarcastic tweets helped amplify institutional criticism and widen public conflict. This study examines event-based automatic detection of provocative political discourse by comparing a feature-based Support Vector Machine (SVM) classifier with a transformer-based IndoBERT model on a large-scale Indonesian Twitter (X) corpus collected from 15 August to 15 September 2025. Tweets were preprocessed and labeled using a rule-based proxy lexicon to distinguish provocative from neutral content, then both models were trained and evaluated under the same experimental setting. Results show that SVM is highly effective for recognizing explicit provocation expressed through repetitive and lexically salient slogans, whereas IndoBERT provides more stable detection of implicit and context-dependent provocation, including irony and sarcasm that are common in Indonesian political talk online. In addition, temporal exploration indicates sharp spikes in tweet volume that align with key offline protest moments, suggesting a close coupling between street-level mobilization and digital discourse dynamics. Overall, the findings support the use of contextual NLP models within event-centered social media analysis to strengthen scalable monitoring of polarization and to inform early-warning approaches for escalating conflict in Indonesia’s digital public sphere.
Evaluasi Celah Keamanan Cross-Site Scripting (XSS) pada Website Menggunakan Black-box Penetration Testing Muhammad Faiz Fadllan; Khairunnisak Nur Isnaini; Ali Nur Ikhsan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3122

Abstract

The xyz.or.id website is a research institution that manages data and information. Given the importance of the data it manages, this website is vulnerable to cyber attacks, especially Cross-Site Scripting (XSS), which can pose serious risks such as data theft and user session hijacking. This study focuses on investigating the security of the input validation mechanism in the registration system. The study aims to identify and analyze security vulnerabilities on the xyz.or.id website using the black-box penetration testing method. The research method includes the stages of information gathering, penetration testing analysis, and reporting. The test results identified a total of 6 security vulnerabilities, classified into 2 high, 1 medium, and 3 low levels. The penetration test analysis found an XSS vulnerability in the “Full Name” input form on the registration page, where the injected payload was successfully executed on the client side. This finding provides empirical evidence that the input validation mechanism and website security policy are not yet optimal. This research resulted in technical recommendations for improvement, including the implementation of input validation, output encoding, and Content Security Policy (CSP) configuration to prevent exploitation by external parties.
KOMPARASI ALGORITMA KNN DAN RANDOM FOREST UNTUK KLASIFIKASI PENYAKIT DISLEKSIA MENGGUNAKAN SMOTE-ENN: COMPARISON OF K-NN AND RANDOM FOREST ALGORITHMS FOR DYSLEXIA DISEASE CLASSIFICATION USING SMOTE-ENN Ali Nur Ikhsan; Pungkas Subarkah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7626

Abstract

The classification of dyslexia has become a significant challenge in the field of artificial intelligence, particularly when dealing with imbalanced datasets between dyslexic and non-dyslexic individuals. This study aims to compare the performance of two machine learning algorithms, namely K-Nearest Neighbors (KNN) and Random Forest (RF), in classifying dyslexia using the SMOTE-ENN (Synthetic Minority Oversampling Technique–Edited Nearest Neighbours) data balancing technique. The dataset was obtained from the Kaggle platform, consisting of 220 initial samples and 197 features. The preprocessing stages included data subsetting, label encoding, and feature normalization using MinMaxScaler, followed by an 80% training and 20% testing data split. The results show that the application of SMOTE-ENN successfully improved the class distribution balance and enhanced the performance of both models. The Random Forest algorithm achieved the best performance with an accuracy of 92.5%, recall of 94.0%, F1-score of 92.5%, and ROC-AUC of 0.97, while KNN achieved an accuracy of 87.5% with a ROC-AUC of 0.90. The improvement in recall and F1-score demonstrates the effectiveness of SMOTE-ENN in enhancing model performance for the minority class. Overall, this study proves that the combination of machine learning algorithms with data balancing techniques can improve classification accuracy and serve as a potential solution for early detection of dyslexia based on cognitive and digital behavioral data.
Sentiment Perspective of Government's Free Nutritious Meal Policy on Social Media X using Indo-BERT and Bi-LTSM Pungkas Subarkah; Ali Nur Ikhsan; Epri Anggraeni; Arbangi Puput Sabaniyah
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1065

Abstract

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, especially in the context of government policies regarding the Free Meal Program that will be implemented throughout Indonesia. This research was conducted using Indo-BERT and Bi-LSTM algorithms. These approaches were used to categorize emotions into three groups: neutral, negative, and positive. Data is obtained from posts on social media X, then after processing the data, it will be applied to both algorithms, namely Indo-BERT and Bi-LSTM. The research findings show that the model's performance in determining the public sentiment of government policies. Validation and valuation were conducted using the f1 score, recall, and precision metrics. The evaluation findings show that the Indo-BERT algorithm is better than the Bi-LSTM algorithm with an accuracy value of 80% for Indo-BERT and 78% for the accuracy value of the Bi-LSTM algorithm, and the Indo-BERT accuracy value is included in the good classification accuracy value. The sentiment analysis results are also represented by word clouds for each positive, negative and neutral class, providing an intuitive picture of the words frequently used in public discourse on free nutritious meals.
Optimizing Multiclass Android Malware Family Classification Using SMOTE-Tomek Links and XGBoost Ali Nur Ikhsan; Adam Prayogo Kuncoro; Debby Ummul Hidayah; Fajar Ramadhan
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1788

Abstract

The increasing sophistication of Android malware attacks has created significant challenges for accurate malware family classification, particularly under highly imbalanced data distributions where minority malware families are frequently misclassified. This study presents a robust multiclass Android malware family classification framework by combining SMOTE-Tomek Links hybrid resampling with an optimized Extreme Gradient Boosting (XGBoost) classifier. The proposed framework addresses two critical issues in previous studies: ineffective handling of minority classes and potential data leakage during resampling and model validation. Experiments were conducted using the CCCS-CIC-AndMal-2020 After Reboot dataset containing 25,059 malware samples distributed across 14 malware families. The proposed approach applies stratified data partitioning, leakage-free SMOTE-Tomek Links integration within an imbalanced-learn pipeline, and RandomizedSearchCV-based hyperparameter optimization with 5-fold stratified cross-validation. Evaluation on an independent holdout test set demonstrates that the optimized framework achieves 80.09% accuracy, 79.85% weighted F1-score, 74.00% macro F1-score, and 97.48% OvR ROC-AUC, outperforming baseline XGBoost and Random Forest models. The results confirm that hybrid resampling combined with optimized gradient boosting improves classification reliability, especially in addressing severe class imbalance and enhancing recognition capability across diverse Android malware families.
Co-Authors Adam Prayogo Kuncoro Alfiansyah Arifudin Akbar Alif Nur Fadilah Arbangi Puput Sabaniyah Dani Arifudin Desri Nur Astri Deuis Nur Astrida Epri Anggraeni Evril Fadrekha Cahyani Fajar Ramadhan Hendra Marcos Hidayah, Debby Ummul Imun Faizal Irma Darmayanti Isnaini, Khairunnisak Nur Jali Suhaman M. Syaiful Amin Muhammad Faiz Fadllan Muhammad Thoriq Jamil Muslimin Hidayat Primandani Arsi Pungkas Subarkah Raditya Sani Alifian Rayinda Maya Anjani Septi Oktaviani Nur Hidayah Siti Alvi Solikhatin Suliswaningsih Syahrul Sani