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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IKRA-ITH Informatika : Jurnal Komputer dan Informatika IJID (International Journal on Informatics for Development) Jurnal Ilmiah Sinus TIN: TERAPAN INFORMATIKA NUSANTARA JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Responsif : Riset Sains dan Informatika Decode: Jurnal Pendidikan Teknologi Informasi Journal Computer Science and Informatic Systems : J-Cosys KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Technologica SENTRI: Jurnal Riset Ilmiah Jurnal Keilmuan dan Keislaman INCOME: Indonesian Journal of Community Service and Engagement Journal of Artificial Intelligence and Digital Business Jurnal Saintifik JUSTIFY : Jurnal Sistem Informasi Ibrahimy Journal of International Multidisciplinary Research Journal of Data Analytics, Information, and Computer Science (JDAICS) Applied Information Technology and Computer Science (AICOMS) Cahaya Pengabdian Journal of Big Data Analytic and Artificial Intelligence Smatika Jurnal : STIKI Informatika Jurnal Jurnal Penelitian Sains Teknologi
Nova Tri Romadloni
Universitas Muhammadiyah Karanganyar
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Klasifikasi Citra Buah Menggunakan Algoritma K-Nearest Neighbour (KNN) dan Metode Euclidean Distance Tristanti, Novi; Romadloni, Nova Tri; Sya’bani, Nur Hayati
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.3243

Abstract

Pengolahan citra digital merupakan salah satu bidang penting dalam computer vision yang berfokus pada interpretasi citra untuk memperoleh informasi bermakna, khususnya dalam proses identifikasi dan klasifikasi objek berbasis karakteristik visual. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk membedakan jenis buah dengan menerapkan algoritma K-Nearest Neighbor (KNN) menggunakan pendekatan Euclidean Distance pada lingkungan MATLAB. Prosedur penelitian dilaksanakan melalui beberapa tahapan utama, meliputi pre-processing, ekstraksi ciri, normalisasi histogram, serta klasifikasi. Pada tahap pre-processing, citra yang menjadi dataset terlebih dahulu dikonversi ke bentuk grayscale, dilanjutkan dengan proses noise reduction dan binarisasi guna meningkatkan kualitas citra serta memperjelas fitur objek. Tahap ekstraksi ciri kemudian dilakukan untuk memperoleh informasi visual yang relevan, sedangkan normalisasi histogram berfungsi menstandarkan distribusi intensitas piksel agar proses klasifikasi menjadi lebih optimal. Hasil pengujian menunjukkan bahwa sistem mampu melakukan pengenalan citra buah dengan tingkat akurasi rata-rata sebesar 93,3%. Capaian ini mengindikasikan bahwa kombinasi metode ekstraksi fitur dengan algoritma KNN cukup efektif dalam mengelompokkan citra berdasarkan kemiripan karakteristik visualnya. Secara praktis, sistem ini berpotensi diterapkan dalam proses sortasi buah secara otomatis pada sektor pertanian maupun industri pengolahan hasil pangan. Meskipun demikian, pengembangan lebih lanjut masih diperlukan, misalnya melalui penambahan variasi dataset, peningkatan kualitas citra, serta penerapan algoritma klasifikasi yang lebih adaptif agar sistem mampu bekerja secara optimal pada kondisi citra yang lebih kompleks dan bervariasi.
CLASSIFICATION OF SMS SPAM WITH N-GRAM AND PEARSON CORRELATION BASED USING MACHINE LEARNING TECHNIQUES Romadloni, Nova Tri; Septiyanti, Nisa Dwi; Pratomo, Cucut Hariz; Kurniawan, Wakhid; Bintang, Rauhulloh Ayatulloh Khomeini Noor
SENTRI: Jurnal Riset Ilmiah Vol. 3 No. 2 (2024): SENTRI : Jurnal Riset Ilmiah, February 2024
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v3i2.2252

Abstract

The Short Message Service (SMS) has garnered widespread popularity due to its simplicity, reliability, and ubiquitous accessibility.This study aims to enhance the efficacy of SMS classification by refining the classification process itself. Specifically, it strives to streamline the process by diminishing feature dimensions and eliminating inconsequential attributes. The textual data undergoes preprocessing, which involves employing the N-Gram technique for feature representation, followed by meticulous feature selection utilizing Pearson Correlation. The study employs 5 of classification algorithms. Notably, the findings underscore that the optimal outcomes emerge from the fusion of the N-Gram methodology with feature selection through Pearson Correlation. Among these, the Support Vector Machine methodology stands out, exhibiting a remarkable 91.41% enhancement in accuracy without feature selection, a further improvement to 91.96% through N-Gram utilization, and a final performance of 70.80% following the inclusion of weighted correlation. However, it is imperative to acknowledge the limitations inherent in the model's generalizability, primarily stemming from the utilization of a relatively modest dataset. Despite the efficacy of Pearson correlation and N-gram-based feature selection in curbing data dimensionality and enhancing processing efficiency, certain pertinent features may have been overlooked, or the chosen attributes might not be optimally suited for specific classifications.
Penyuluhan Cerdas Literasi Digital dalam Menghadapi Penyebaran Berita Hoaks pada Anggota Bhayangkari Tri Romadloni, Nova; Supriyanti, Wiwit
INCOME: Indonesian Journal of Community Service and Engagement Vol 2 No 2 (2023)
Publisher : EDUPEDIA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56855/income.v2i2.402

Abstract

The wide and rapid spread of hoax news via the internet, social media or other digital platforms has become a serious problem in society, including among Bhayangkari members. This community service has the goal of making digital literacy smart in helping Bhayangkari members deal with the spread of hoax news. This training aims to provide the understanding and skills needed to recognize, analyze, and respond wisely to hoax news. This counseling process is an experimental pretest-posttest given to Bhayangkari members. The results show that digital literacy smart counseling has a positive impact on increasing the knowledge and skills of Bhayangkari members in dealing with the spread of hoax news. This is about knowledge about hoax news, the ability to verify information, and awareness of the consequences of spreading hoax news. Bhayangkari members from the Jatiyoso branch who attended counseling were more skeptical of unverified information, were more careful in disseminating information, and were more active in checking the truth of news before believing it.
ANALISIS DAMPAK CACHE PROGRESSIVE WEB APPS TERHADAP KONSUMSI BATERAI ANDROID Kurniawan, Wakhid; Romadloni, Nova Tri; Noor Bintang, Rauhulloh Ayatulloh Khomeini
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6221

Abstract

Penggunaan aplikasi web berkembang pesat, terutama di Android yang menguasai 46,18% pangsa pasar global. Pengguna menginginkan akses cepat, namun sering menghadapi koneksi lambat dan pemuatan ulang aset tanpa cache, yang dapat meningkatkan konsumsi baterai. Salah satu faktor yang diduga berpengaruh adalah penggunaan cache dalam aplikasi. Progressive Web Apps (PWA) menjadi relevan karena memanfaatkan service worker untuk menyimpan cache. PWA menawarkan keunggulan seperti akses tanpa koneksi, pemrosesan latar belakang, dan notifikasi push, memberikan pengalaman serupa aplikasi native. Penelitian ini menganalisis dampak cache PWA terhadap konsumsi baterai Android. Metode yang digunakan bersifat kuantitatif dengan eksperimen empiris. Sebanyak 33 situs PWA dipilih menggunakan Google Lighthouse. Data ukuran cache dikumpulkan, dan laporan bug dihasilkan selama 3 menit untuk mengukur konsumsi daya. Analisis dilakukan menggunakan uji Paired Sample T-Test dengan SPSS, membandingkan konsumsi baterai saat cache kosong dan terisi. Penelitian ini bertujuan memberikan wawasan mengenai pengaruh cache terhadap konsumsi daya, sehingga strategi dapat dikembangkan untuk meningkatkan efisiensi energi dan pengalaman pengguna.
PERBANDINGAN KINERJA ALGORITMA KLASIFIKASI PADA REVIEW PENGGUNA APLIKASI NETFLIX KHOMEINI NOOR BINTANG, RAUHULLOH AYATULLOH; Romadloni, Nova Tri
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6303

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Netflix yang diperoleh dari Google Play Store menggunakan metode web scraping dengan Python di Google Colab. Data ulasan diproses melalui tahap pembersihan teks, tokenisasi, penghapusan stopword, dan stemming, serta direpresentasikan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Lima algoritma klasifikasi, yaitu Logistic Regression, Naive Bayes, Decision Tree, Random Forest, dan Support Vector Machine (SVM), dibandingkan untuk menentukan algoritma terbaik dalam klasifikasi sentimen positif, negatif, dan netral. Evaluasi dilakukan berdasarkan akurasi dengan pembagian data latih dan data uji sebesar 90:10. Hasil pengujian menunjukkan bahwa Logistic Regression dan Random Forest memiliki akurasi tertinggi sebesar 76%, diikuti oleh SVM sebesar 74%, Decision Tree sebesar 73%, dan Naive Bayes dengan akurasi terendah sebesar 71%. Temuan ini memberikan kontribusi bagi penelitian di bidang analisis sentimen serta dapat menjadi referensi bagi pengembang aplikasi dalam meningkatkan pengalaman pengguna berbasis data.
Perbandingan Algoritma Klasifikasi Terhadap Review Aplikasi Maxim Menggunakan Teknik Klasifikasi Machine learning Romadloni, Nova Tri; Mulia, Pamela Hana; Supriyanti, Wiwit
Technologica Vol. 5 No. 1 (2026): Technologica
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/technologica.v5i1.268

Abstract

Perkembangan teknologi telah membawa dampak besar terhadap sistem transportasi publik, salah satunya dengan munculnya layanan ojek online yang memungkinkan pemesanan melalui aplikasi digital. Salah satu layanan transportasi daring yang cukup populer adalah Maxim, yang telah diunduh lebih dari 50 juta kali. Ulasan pengguna terhadap aplikasi ini menjadi sumber informasi penting untuk menilai pengalaman mereka serta sebagai dasar dalam upaya peningkatan kualitas layanan. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja beberapa algoritma machine learning dalam mengklasifikasikan sentimen dari ulasan pengguna Maxim. Data dikumpulkan menggunakan metode web scraping dan dikelompokkan berdasarkan rating bintang. Tahapan pra-pemrosesan mencakup pembersihan teks, tokenisasi, stemming, dan pembobotan menggunakan metode TF-IDF. Algoritma yang digunakan meliputi Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), dan Random Forest (RF). Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Berdasarkan hasil analisis, algoritma Random Forest menunjukkan kinerja terbaik dengan akurasi mencapai 95%. Secara umum, hasil penelitian ini menegaskan bahwa Random Forest unggul dibandingkan algoritma lain dalam menganalisis sentimen ulasan pengguna aplikasi Maxim
A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
IJID (International Journal on Informatics for Development) 2025
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
Evaluasi Kualitas Website SEGO PETANI terhadap Kepuasan Pengguna Menggunakan WebQual 4.0 Desi Lajar Sari; Nova Tri Romadloni
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9940

Abstract

This study aims to evaluate the quality of the SEGO PETANI website using the WebQual 4.0 method and analyze its effect on user satisfaction. The SEGO PETANI website is a digital platform designed to help farmers report pest and plant disease attacks in order to support the effectiveness of technology-based agricultural services. The research was conducted at Dinas Pertanian Pangan dan Perikanan Kabupaten Karanganyar involving 167 respondents who had used the SEGO PETANI website. This study employed a quantitative approach with a descriptive-verificative design and purposive sampling technique. Data were collected through questionnaires based on a five-point Likert scale developed from the dimensions of usability quality, information quality, and service interaction quality. Data analysis was performed using SPSS through validity testing, reliability testing, multiple linear regression, t-test, F-test, and coefficient of determination analysis. The results showed that the website quality was categorized as good, with average scores of 4.00 for usability quality, 4.03 for information quality, and 4.06 for service interaction quality. Website quality significantly affected user satisfaction with a coefficient of determination value of 71.1%. Service interaction quality was identified as the most dominant factor influencing user satisfaction. This study provides practical contributions as an evaluation material for improving digital agricultural services and academic contributions through the implementation of the WebQual 4.0 method in public service-based information systems.
Sistem Penjadwalan Ruangan Berbasis Website dengan Validasi Benturan Jadwal Salma Nurul Ikhsani; Nova Tri Romadloni
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9995

Abstract

The utilization of information technology holds a vital position in elevating the excellence of data management and services in government agencies. This research was conducted at the Samber Nyawa Information Center of the Karanganyar Regency Communication and Information Service, which continues to face challenges in managing room utilization. The problem is that the current room scheduling procedure is carried out conventionally, often leading to overlapping schedules, information distribution constraints, and low efficiency in monitoring room utilization for both domestic and public events. This investigation objectifies to model and build a website-based room scheduling information system to facilitate the room scheduling mechanism in a more proficient and consolidated layout. The platform development deploys the Waterfall model, which comprises the phases of prerequisite analysis, system design, execution, verification, and maintenance. The application is structured leveraging the Laravel framework alongside a MySQL database and executes the Model View Controller (MVC) pattern. Strategies for gathering information are executed via observation, questions and answers, and literature review. The system validation utilized a User Acceptance Testing (UAT) scheme involving 20 participants consisting of Karanganyar Regency Communications and Information Technology (Diskominfo) employees, students, interns, and general users who had tried the website-based room scheduling information system. The examination findings showed that the system achieved a user approval rating of 89%, categorized as very good. The finalized software is capable of facilitating the progression of managing room usage schedules and provides real-time and structured room availability information for users.
Perbandingan Random Forest dan SVM pada Analisis Sentimen Reformasi Pendidikan Nur Hayati; Nova Tri Romadloni
Journal of Big Data Analytic and Artificial Intelligence Vol 9 No 1 (2026): JBIDAI Juni 2026
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v9i1.92

Abstract

This study aims to compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in conducting sentiment analysis on YouTube comments related to education reform in Indonesia. The dataset used in this study consisted of 981 comments collected from the YouTube platform, and randomly labeled with two categories: "positive" and "negative." The labeling process was carried out using Microsoft Excel, while data processing was carried out using RapidMiner software. Model evaluation was carried out using the cross-validation method to obtain more objective results and avoid overfitting. The results showed that the Random Forest algorithm obtained an accuracy of 99.87% ± 0.40% with a micro average of 99.87%, while the SVM algorithm produced an accuracy of 90.58% ± 3.78% with a micro average of 90.57%. Based on these results, it can be concluded that Random Forest has superior performance in classifying comment sentiment compared to SVM. This is due to Random Forest's ability to combine several decision trees to produce more stable and accurate predictions. The findings of this study can be a reference for other researchers in selecting the right algorithm for sentiment analysis on text data, especially in the context of education and public opinion.
Co-Authors Adi Jufriansah Ammar Shafiy Audy Trinugroho Listyawan Avianti, Risqi Nur Azmi Khusnani Budilaksono, Sularso Burhan Efendi Cucut Hariz Pratomo Cucut Hariz Pratomo David Febrianto Desi Lajar Sari Edwin Ariesto Umbu Malahina Efendi, Burhan Erwinda Sam Anafih Fanani, Galih Pramuja Inngam Fatimah Hasna Karima Hana Mulia, Pamela Handoko, Frendy Prasetyo Hani Setiani Hendri Setiawan Hilman F Pardede IMAM SANTOSO Irwan Akib Kurniaji, Ganno Tri Buana Kurniawan, Wakhid Lenny Puspita Dewi Mariyanto Mariyanto Mariyanto Miswanto Muhammad Ardiansyah Muhammad Irfan Luthfi Muhammad Qolbi Shobri Muhammad Syahriandi Adhantoro Muhammad Yusuf Ariyadi Mukhlis Mukhlis Mulia, Pamela Hana Mulyawati, Theana Lativa Nashiruddin, Abid Naufal Ishartono Nawwar, Nabil Abiyu Nisa Dwi Septiyanti Nova Ellyanza, Marcellina Nugraheni, Anidhya Siwi Nur Hayati Nurul Iksani, Salma Osniman Paulina Maure Pratomo, Cucut Hariz Primananda, Masdicka Maulana Rahmawati, Aderima Rauhulloh Ayatulloh Khomeini Noor Bintang Resi Intan Penatari Rianti , Ria Rika Putri Utami Saefudin Salma Nurul Ikhsani Samsi Samsi Sari, Desi Lajar Setyawati, Yayuk Sholefah, Wahyu Putri Sya’bani, Nur Hayati Tanti Diyah Rahmawati Tristanti, Novi Wahyu Putri Sholefah Wakhid Kurniawan Wakhid Kurniawan Wiwit Supriyanti YAN ADITYA PRADANA Yayu Sri Rahayu Yitno Puguh Martomo