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All Journal Jurnal Sains dan Teknologi Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknologi Sistem Informasi dan Aplikasi JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Infotekmesin 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
APLIKASI DATA MINING MENGGUNAKAN METODE APRIORI UNTUK MENGIDENTIFIKASI POLA PEMBELIAN DI TOKO ANANDA BARU Fidela, Anindya; Widiawati, Chyntia Raras Ajeng; Ikhsan, Ali Nur
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7700

Abstract

Dalam industri ritel, mengenali pola perilaku pengguna adalah hal yang penting untuk meningkatkan efektivitas dalam pemasaran dan manajemen persediaan. Studi ini bertujuan untuk menerapkan teknik data mining dengan menggunakan algoritma Apriori untuk menemukan pola pembelian konsumen di Toko Ananda Baru. Data yang digunakan diambil dari 7.851 transaksi yang terjadi pada bulan Juni 2024, yang kemudian dianalisis melalui proses pra-pengolahan, analisa itemset yang sering muncul, pembuatan aturan asosiasi, dan pengukuran rasio lift menggunakan Google Colaboratory dan bahasa pemrograman Python. Hasil dari studi ini menunjukkan bahwa terdapat beberapa kombinasi produk dengan asosiasi yang kuat, ditandai dengan nilai lift ratio sebesar 76,5, yang jauh lebih tinggi dibandingkan studi serupa di sektor ritel konvensional. Produk Azarine Moist Sunserum 100ML menjadi item dengan tingkat asosiasi tertinggi, sering dibeli bersamaan dengan berbagai produk lain. Temuan ini memberikan saran bagi pengelola toko untuk menerapkan strategi bundling, cross-selling, serta pengaturan produk yang lebih baik untuk meningkatkan penjualan dan kepuasan konsumen. Selain itu, penelitian ini juga merekomendasikan agar analisis data transaksi dilakukan secara rutin dan mengantisipasi penggunaan algoritma alternatif seperti FP-Growth untuk data yang lebih besar. Penerapan algoritma Apriori terbukti efektif dalam membantu usaha ritel kecil bersaing di tengah ketatnya persaingan bisnis melalui pendekatan berbasis data.
Co-Authors Adam Prayogo Kuncoro Ajeng Widiawati, Chyntia Raras Alif Nur Fadilah Alifah Dafa Iftinani Alifian , Raditya Sani Chyntia Raras Ajeng Widiawati Dias Ayu Budi Utami, Dias Ayu Budi Fadilah, Alif Nur Faizah, Nailatul Fatiha, Muhammad Reza Fidela, Anindya Hidayat, Muslimin Iftinani, Alifah Dafa Irma Darmayanti Isnaini, Khairunnisak Nur Maulana Baihaqi, Wiga Naufal Yogi Aptana Pungkas Subarkah Rakhmawati, Desty Rizki Wahyudi Rofiqoh, Dayana Setiawan, Ito Shafira, Lulu Siti Alvi Solikhatin Suhaman, Jali Wibowo, Adlan Yuliastuti, Lulu Yunita, Ika Romadoni