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All Journal Jurnal Sains dan Teknologi Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknologi Sistem Informasi dan Aplikasi JURTEKSI Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Infotekmesin Journal of Information Systems and Informatics Journal of Technology and Informatics (JoTI) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Ikhsan, Ali Nur
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering 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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Analisis Sentimen Media Sosial X Terhadap Kenaikkan PPN di Indonesia Menggunakan Algoritme Naïve Bayes dan Support Vector Machine (SVM) Ikhsan, Ali Nur; Pungkas Subarkah; Alifah Dafa Iftinani; Alif Nur Fadilah
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2518

Abstract

One of the ways to increase state revenue is by raising the Value-Added Tax (VAT). However, implementing a VAT hike policy often elicits both positive and negative responses from the public. With the presence of social media, people can voice their opinions about government policies, including through social media platform X. This study aims to analyze public sentiment on social media X using the Naïve Bayes and Support Vector Machine (SVM) algorithms. The research compares the highest accuracy results before and after the balancing process. The dataset comprises 2,852 rows in CSV format. The findings indicate that the SVM algorithm achieves an accuracy of 98% before balancing and 97% after balancing, while Naïve Bayes achieves an accuracy of 97% before balancing and 90% after balancing. Overall, both algorithms demonstrate good and balanced performance.
Perbandingan Random Forest dan K-Nearest Neighbors untuk Klasifikasi Body Mass Index Menggunakan SMOTE-ENN untuk Mengatasi Ketidakseimbangan Data pada Analisis Kesehatan Naufal Yogi Aptana; Ikhsan, Ali Nur; Maulana Baihaqi, Wiga; Ajeng Widiawati, Chyntia Raras
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2553

Abstract

This study aims to compare the Random Forest and K-Nearest Neighbors (KNN) algorithms in Body Mass Index (BMI) classification using the SMOTE-ENN method to address data imbalance. The dataset consists of 2111 entries with demographic and health attributes of individuals. Data imbalance poses a significant challenge that may affect the accuracy of machine learning models. The SMOTE-ENN combination was employed to improve data distribution, enabling models to recognize patterns in minority classes better. Key evaluation factors included both algorithms' accuracy, precision, recall, and F1-score. Results indicate that the Random Forest algorithm achieved higher performance with 100% accuracy than KNN with 96% after applying SMOTE-ENN. These findings highlight the unique contribution of SMOTE-ENN in handling imbalanced data, enhancing classification model quality, and significantly impacting machine learning applications in healthcare.
Performance Comparison of Decision Tree J48, CART, and Naïve Bayes Algorithms for Predicting Chronic Kidney Disease Ikhsan, Ali Nur; Fadilah, Alif Nur; Iftinani, Alifah Dafa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26472

Abstract

Chronic Kidney Disease could be a worldwide issue that proceeds to extend with high treatment costs. Accurate diagnosis is essential for managing this disease. There is a requirement for a technique to anticipate chronic kidney disease, with prevalent use being made of Decision Tree J48, Naive Bayes, and CART algorithms which offer benefits like swift computation, ease of use, and high precision. The researchers aimed to determine the comparison results of Decision Tree J48, CART, and Naive Bayes algorithms for predicting chronic kidney disease. From the research findings, it was concluded that the CART algorithm had the highest accuracy rate of 97.25% in predicting chronic kidney disease, compared to the J48 Decision Tree algorithm and the Naïve Bayes algorithm with accuracy rates of 96.5% and 93.5% respectively. The CART algorithm can be utilized by pathologists to develop a program for predicting chronic kidney disease.
Development of UI/UX Application of Javanese Script Using User Centered Design in Elementary School Wibowo, Adlan; Rakhmawati, Desty; Ikhsan, Ali Nur
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 2 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i2.39247

Abstract

Indonesia is a country with an area of 1.9 million km², rich in diversity, including various languages in each region. This linguistic diversity gives each region its unique characteristics. However, in the 21st century, there is a noticeable decline in the use of regional languages, particularly in polite speech. This decline is attributed to the influence of Western culture, which has permeated Indonesia through technological advancements and television broadcasts showcasing urban and foreign cultures. The Javanese language, known for its unique *unggah-ungguh* (levels of politeness) and distinctive script, is one of the cultures at risk. The rapid development of technology, especially in education, necessitates its optimal utilization while preserving ancestral heritage such as the Javanese language. This study focuses on designing an optimal User Interface (UI) and User Experience (UX) for a Javanese language learning application on Android devices. The application includes features such as wulangan (lessons), pitakonan (questions), and tembang (traditional songs), with a focus on the User-Centered Design (UCD) approach. The research object is MI Nurul Islam, an elementary school in Brebes Regency. After the UI/UX design was successfully created, the System Usability Scale (SUS) was used to evaluate its effectiveness. The design received an average SUS score of 77.5, indicating that it is well-suited for further application development. The findings suggest that the designed application can significantly aid in preserving and promoting the use of the Javanese language among early childhood learners, addressing the decline caused by external cultural influences.
OPTIMIZATION OF CART ALGORITHM BASED ON ANT BE COLONY FEATURE SELECTION FOR STUNTING DIAGNOSIS Subarkah, Pungkas; Ikhsan, Ali Nur; Wahyudi, Rizki; Rofiqoh, Dayana
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 2 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3579

Abstract

Abstract: One of the main health problems in children is stunting which is one of the concerns in the Sustainable Development Goals (SDGs). Specifically in Indonesia, the prevalence of stunting in 2024 is 21.6%. This figure is still relatively high, because the target prevalence of stunting is 14%. This study aims to implement machine learning knowledge through the Classification And Regression Trees (CART) algorithm based on Ant Be Colony (ABC) feature selection which aims to determine the increase in accuracy in analyzing stunting datasets. The data used comes from Kaggle which consists of 16500 datasets. The dataset consists of gender, age, birth length, birth weight, body length, body weight, breastfeeding and stunting status. The research methods used are data collection, data preprocessing, classification, and evaluation using K-fold cross validation. The results obtained in this research are the implementation of the CART algorithm obtained a value of 89.86% and the results of CART with Ant Be Colony (ABC) feature selection, which obtained an accuracy value of 93.65%. This shows that there is an increase in the accuracy value in the use of CART algorithm optimization and Ant Be Colony (ABC) feature selection by 3.76%. With the research results that have been obtained, it can be categorized as excellent accuracy value excellent. It is hoped that further research can be carried out by adding other classification algorithms or adding feature selection.            Keywords: classification; feature selection; optimazation; stunting Abstrak: Salah satu masalah kesehatan utama pada anak adalah stunting yang menjadi salah satu perhatian dalam Sustainable Development Goals (SDGs). Khusus di Indonesia angka Pravelensi stunting pada tahun 2024 di angka 21.6%. Angka ini masih tergolong tinggi, karena target angka pravelensi stunting ialah 14%. Penelitian ini bertujuan untuk mengimplementasikan pengetahuan machine learning melalui algoritma Classification And Regression Trees (CART) berbasis seleksi fitur Ant Be Colony (ABC) yang bertujuan untuk mengetahui peningkatan akurasi dalam menganalisis dataset stunting. Data yang digunakan bersumber dari Kaggle yang terdiri dari 16500 dataset. Dataset terdiri dari jenis kelamin, usia, panjang lahir, berat lahir, panjangg badan, berat badan, menyusui dan status stunting.  Metode penelitian yang digunakan adalah pengumpulan data, preprocessing data, klasifikasi, dan evaluasi menggunakan K-fold cross validation. Hasil yang diperoleh pada penelitian ini adalah Implementasi algoritma CART memperoleh nilai sebesar 89,86% dan hasil seleksi fitur CART dengan Ant Be Colony (ABC) memperoleh nilai akurasi sebesar 93,65%. Hal ini menunjukkan adanya peningkatan nilai akurasi pada penggunaan optimasi algoritma CART dan pemilihan fitur Ant Be Colony (ABC) sebesar 3,76%. Dengan hasil penelitian yang telah diperoleh dapat dikategorikan nilai akurasi yang diperoleh sangat baik. Diharapkan dapat dilakukan penelitian selanjutnya dengan menambahkan algoritma klasifikasi lain atau menambahkan seleksi fitur. Kata kunci: klasifikasi; optimalisasi; seleksi fitur; stunting
Sentiment Perspective of Government's Free Nutritious Meal Policy on Social Media X using Indo-BERT and Bi-LTSM Subarkah, Pungkas; Ikhsan, Ali Nur; Anggraeni, Epri; Sabaniyah, Arbangi Puput
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.
PEMBERDAYAAN INDUSTRI KAYU MELALUI INOVASI PRODUK LIMBAH DAN PENDEKATAN EKONOMI SIRKULAR Arifudin, Dani; Ikhsan, Ali Nur; Firdauzi, Indrawan; Robbani, Ayat Akras; Aeni, Alfina Nur; Cahyo, Samsul Dwi
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 6 (2025): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i6.34862

Abstract

Abstrak: Industri kayu “Hikmah Padi” menghasilkan limbah potongan dan serbuk kayu yang belum dimanfaatkan secara optimal dan berpotensi mencemari lingkungan sekitar. Kegiatan pengabdian ini bertujuan untuk memberdayakan pengrajin kayu melalui peningkatan hard skill dalam pengolahan limbah kayu menjadi produk bernilai guna serta soft skill dalam manajemen usaha dan pemasaran digital berbasis prinsip ekonomi sirkular. Metode pelaksanaan meliputi observasi lapangan, diskusi dengan mitra, pelatihan teknis, proses produksi, dan pendampingan dalam pengembangan branding. Evaluasi dilakukan menggunakan kuesioner dan lembar observasi dengan skala Likert untuk membandingkan kondisi sebelum dan sesudah pelatihan pada aspek produksi, manajemen, dan pemasaran. Mitra kegiatan terdiri atas 15 pengrajin kayu yang tergabung dalam industri lokal. Hasil kegiatan menunjukkan peningkatan keterampilan rata-rata sebesar 39%, dengan peningkatan tertinggi pada aspek branding dan pemasaran digital sebesar 45%. Program ini menghasilkan identitas produk “KAYURUPA”, memperluas promosi melalui platform digital, serta memperkuat daya saing dan keberlanjutan ekonomi masyarakat desa.Abstract: The “Hikmah Padi” wood industry generates wood waste in the form of scraps and sawdust that has not been optimally utilized and potentially pollutes the surrounding environment. This community service program aims to empower artisans by improving their hard skills in processing wood waste into value-added products and soft skills in business management and digital marketing based on the principles of the circular economy. The implementation methods include field observation, partner discussions, technical training, production processes, and branding assistance. Evaluation was conducted using questionnaires and observation sheets with a Likert scale to compare pre- and post-training conditions in production, management, and marketing aspects. The partner group consists of 15 local wood artisans. The results show an average skill improvement of 39%, with the highest increase of 45% in branding and digital marketing. The program produced the “KAYURUPA” product identity, expanded promotion through digital platforms, and strengthened local competitiveness and community economic sustainability.
Event-Based Detection of Provocative Political Discourse on Indonesian Twitter: A Comparative Study of SVM and IndoBERT Cahyani, Evril Fadrekha; Ikhsan, Ali Nur; Astrida, Deuis Nur
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.
Co-Authors Adam Prayogo Kuncoro Aeni, Alfina Nur Ajeng Widiawati, Chyntia Raras Alif Nur Fadilah Alifah Dafa Iftinani Alifian , Raditya Sani Anggraeni, Epri Astrida, Deuis Nur Cahyani, Evril Fadrekha Cahyo, Samsul Dwi Dani Arifudin Dias Ayu Budi Utami, Dias Ayu Budi Fadilah, Alif Nur Faizah, Nailatul Fatiha, Muhammad Reza Hidayat, Muslimin Iftinani, Alifah Dafa Indrawan Firdauzi Irma Darmayanti Isnaini, Khairunnisak Nur Maulana Baihaqi, Wiga Naufal Yogi Aptana Pungkas Subarkah Rakhmawati, Desty Rizki Wahyudi Robbani, Ayat Akras Rofiqoh, Dayana Sabaniyah, Arbangi Puput Setiawan, Ito Shafira, Lulu Siti Alvi Solikhatin Suhaman, Jali Wibowo, Adlan Yuliastuti, Lulu Yunita, Ika Romadoni