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All Journal Jurnal Informatika Jurnal Ilmiah Matrik JURNAL ILMIAH INFORMATIKA Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Abdimas Mahakam METIK JURNAL Building of Informatics, Technology and Science Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sains Komputer dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Terpadu International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Informatika Teknologi dan Sains (Jinteks) Sci-Tech Journal Sewagati: Jurnal Pengabdian Masyarakat Indonesia Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Publikasi Teknik Informatika (JUPTI) Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) GEMBIRA (Pengabdian Kepada Masyarakat) Buffer Informatika JSE Journal of Science and Engineering
Naufal Azmi Verdikha
Teknik Informatika, Universitas Muhammadiyah Kalimantan Timur
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PERFORMANCE OF TEXT SIMILARITY ALGORITHMS FOR ESSAY ANSWER SCORING IN ONLINE EXAMINATIONS Susanto, Muhammad Riza Radyaka; Husni Thamrin; Naufal Azmi Verdikha
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1025

Abstract

The purpose of assessment is to determine learning success. Exams with question descriptions have several advantages, including ease of preparation and the ability to reveal student comprehension and originality. The problem with space is that it takes time to fix. Therefore, it is important to develop algorithms and software that automatically evaluate space. With the help of this algorithm and this software, you can solve some exam and assessment problems. This study aims to investigate similarity algorithms that approximate human patterns in evaluating ambiguous answers. This study examines his five similarity algorithms, including TF-IDF and LSA. The data was a collection of correct answers with a total of 371 texts. The similarity algorithm's performance was compared with human correction results. Evaluation was performed using Root Mean Square Error (RMSE). This study shows that his TF-IDF algorithm like Jaccard has the lowest his RMSE compared to human judgement. However, the LSA algorithm tended better to follow human rating patterns for descriptive tests..
PEMBUATAN APLIKASI ABSENSI DAN DOORPRIZE PENGUNJUNG BAPPEDA BERBASIS WEB PADA EVENT KALTIM EXPO 2023 Nurdiansyah, Rendy; Takhta Perlawanan Putra Sinawang; Reza Andriyanti; Naufal Azmi Verdikha
Jurnal Gembira: Pengabdian Kepada Masyarakat Vol 1 No 06 (2023): DESEMBER 2023
Publisher : Media Inovasi Pendidikan dan Publikasi

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

Abstract

Badan Perencanaan Pembangunan Daerah (BAPPEDA) adalah lembaga penting dalam perencanaan dan pembangunan daerah di Indonesia. Keterlibatan BAPPEDA dalam Kaltim Expo, sebagai bagian dari peringatan Hari Ulang Tahun Republik Indonesia ke-78, melibatkan presentasi kinerja serta hasil pencapaian, serta penyelenggaraan kuis dan undian doorprize untuk menarik perhatian pengunjung. Dalam upaya meningkatkan pengalaman pengunjung, BAPPEDA membutuhkan penggunaan teknologi terkini dengan sistem absensi yang efisien dan menarik, serta integrasi hadiah-hadiah menarik ke dalam sistem tersebut. Untuk memenuhi kebutuhan tersebut, dilakukan perancangan sistem absensi dan aplikasi doorprize berbasis web. Hal ini bertujuan untuk mendapatkan data absensi pengunjung secara efisien, memudahkan proses, dan menciptakan pengalaman yang lebih menarik bagi pengunjung KALTIM EXPO 2023. Pengembangan sistem absensi dan doorprize ini menggunakan metode Software Development Life Cycle (SDLC) dengan pendekatan Agile, fokus pada kerja tim kolaboratif yang responsif terhadap perubahan. Penggunaan bahasa pemrograman HTML, PHP, CSS, JS, dan Bootstrap 5 digunakan untuk tampilan, serta MySQL sebagai basis data.
Multilayer Perceptron and TF-IDF in the Classification of Hate Speech on Twitter in Indonesian Syahrandi, Akmal; Latipah, Asslia Johar; Verdikha, Naufal Azmi
JSE Journal of Science and Engineering Vol. 2 No. 1 (2023): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v1i1.3773

Abstract

Twitter nowadays is one of the popular social media which currently has over 300millions accounts, twitter is the rich source to learn about people’s opion and sentimental analysis. However, this also brings new problems where the practice of hate speech. This research classifies of hate speech on social media. Evaluation using dataset from previous research Ibrohim&Budi (2019), then using classification method Multilayer Perceptron which combined with feature extraction to be able to detect negations and weighting uses Term Frequency – Inverse Document Frequency (TF-IDF). Results show that the F1 score gives an accuracy rate of up to 74.51%. This research has a reasonably good effectiveness from combining the TF-IDF and Multilayer Perceptron methods, considering the results obtained from the F1 Score evaluation value.
Indonesian Automated Essay Scoring with Bag of Word and Support Vector Regression Verdikha, Naufal Azmi; Dwiagam, Junianda Haris; Hasudungan, Rofilde
JSE Journal of Science and Engineering Vol. 2 No. 2 (2024): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v1i2.3841

Abstract

Essay is one of the test questions to measure students' understanding of learning. Respondents can organize the answers to each question in their own language style, so it takes time to make corrections. It takes a system that can assess essay answers automatically quickly and accurately. Auto Essay Scoring (AES) is a tool that can assign grades or scores to answers in the form of essays automatically. In giving grades automatically, AES requires machine learning with training data that contains answer data that has been given a value by the assessor. In this study, AES was used to assess the Indonesian language midterm exams using the Bag of Word extraction feature and using Support Vector Regression. The Root Mean Square Error value obtained when evaluating AES is 1.99.
Komparasi Ekstraksi Fitur TF-IDF dan Word2Vec pada Naïve Bayes untuk analisis Sentimen Pembangunan IKN di YouTube Rahmad Fahrozi, Mu. Aldi; Siswa, Taghfirul Azhima Yoga; Verdikha, Naufal Azmi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9198

Abstract

The development of Indonesia’s New Capital City (IKN) has generated diverse public responses on social media, particularly YouTube, making sentiment analysis necessary to map public perceptions. Previous studies have reported relatively low classification accuracy, reaching only 60%, indicating the need for more effective approaches to improve performance. This study aims to compare the performance of the Naïve Bayes algorithm in classifying public sentiment toward the IKN development using two feature extraction methods, namely TF-IDF and Word2Vec. The data were collected from YouTube comments and processed through preprocessing stages, expert-based labeling, and evaluation using 10-Fold Cross Validation. The results show that the TF-IDF-based Multinomial Naïve Bayes model achieves the best performance with an accuracy of 83%, a positive recall of 82%, and a negative F1-score of 85%, outperforming the Word2Vec-based Gaussian Naïve Bayes model, which attains an accuracy of 82% with a lower positive recall of 76%. These findings confirm that TF-IDF is more effective and stable in handling short-text comment characteristics than Word2Vec, which requires a larger corpus for optimal semantic representation.
Penguatan Literasi Digital dan Manajemen Pokdarwis untuk Pengembangan Geowisata dan Budaya Desa Sungai Bawang Alam, Fajar; Ismunandar, Wisnu; Verdikha, Naufal Azmi
Jurnal Abdimas Mahakam Vol. 10 No. 01 (2026): Januari
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v10i01.3763

Abstract

Program pengabdian masyarakat ini bertujuan meningkatkan kapasitas kelompok sadar wisata (Pokdarwis) Desa Sungai Bawang, Kecamatan Muara Badak, Kutai Kartanegara, Kalimantan Timur, dalam mengelola potensi geowisata dan budaya lokal melalui literasi digital serta penguatan manajerial. Desa ini kaya dengan budaya Dayak Kenyah dan Bahau serta lanskap rawa–perbukitan yang potensial, tetapi pengembangannya terkendala oleh rendahnya keterampilan digital dan kelembagaan Pokdarwis. Metode pelaksanaan terdiri dari lima tahapan: sosialisasi, pelatihan literasi digital dan manajemen, penerapan teknologi, pendampingan-evaluasi, serta penyusunan rencana keberlanjutan. Instrumen yang digunakan meliputi tripod, microphone clip-on, papan tulis, papan informasi, kursi lipat. Sebanyak 23 peserta terlibat aktif dalam praktik pemetaan digital, pembuatan video promosi, dan penyusunan rencana kerja Pokdarwis. Hasil menunjukkan peningkatan literasi digital masyarakat dari <40% menjadi >80%, terbentuknya konten promosi wisata (video, foto, narasi), serta perbaikan tata kelola kelembagaan Pokdarwis. Kegiatan ini menegaskan bahwa penerapan teknologi sederhana dan manajemen berbasis komunitas dapat memperkuat identitas wisata budaya dan geowisata desa.
IMPLEMENTATION OF GEMINI PRE-PROCESSING ON 2024 SIREKAP REVIEWS USING THE RANDOM FOREST ALGORITHM Amru Omar; Naufal Azmi Verdikha; Muhamad Ridwan
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 3 (2026): FEBRUARY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18939969

Abstract

This study aims to classify reviews of the SIREKAP 2024 application by utilizing Large Language Model (LLM)-based Gemini pre-processing, Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction, and the Random Forest algorithm as the classification method. The data used consist of user reviews obtained from the Google Play Store and categorized into five rating classes. Model performance evaluation was conducted using the 10-Fold Cross-Validation method with the Macro F1-Score metric. The testing results indicate that the lowest F1-Score achieved was 31.87%, while the highest reached 37.28%, with an overall average Macro F1-Score of 34.62%. These findings demonstrate that the Random Forest algorithm is capable of producing relatively stable classification performance through its ensemble learning mechanism, which combines multiple decision trees. However, its performance is still influenced by the imbalance in data distribution across classes. Therefore, Random Forest plays a role in maintaining prediction stability and reducing overfitting, although further development is required to improve classification performance on imbalanced review data
Co-Authors Abdul Rahim Achmad, Arda Fahmi Adhistya Erna Permanasari Ahmad Ilham Ahmad Ridhani Mubaraq Alam, Fajar Amru Omar Arbansyah Aspianur Asslia Johar Latipah Assliah Johar Latipah Bulan Suci Cahayawati Dwiagam, Junianda Haris Evitasari, Yuliana Dilla Fahrezi, Irqi Anbi Faldi Faldi Faldi Faldi Fendy Yulianto Fiqri, Mohammad Hiqmal Fitri Damayanti Habid, Reza Hasudungan, Rofilde Hendra Saputra Hery Tri Waloyo Hidayati Ramadhani, Novia Husni Thamrin Ilham, Muhammad Fauzan Nur Johar Latipah, Asslia Latipah, Asslia Johar Muhamad Ridwan Muhamad Wahyu Tirta Muhammad Najeri Al Syahrin Muhammad Taufiq Sumadi Muhammad Taufiq Sumadi Muthmainnah Muthmainnah Nuraini, Nabila Nurdiansyah, Rendy Octaviany, Dinda Nur Pranata, Aditya Pranata, Ricky Albin Rahmad Fahrozi, Mu. Aldi Reza Andriyanti Ridhoi, Reno Ririn Wahyuni Rudiman Rudiman, Rudiman Sari, Septa Intan Permata Sayekti Harits Suryawan Susanto, Muhammad Riza Radyaka Syahrandi, Akmal Taghfirul Azhima Yoga Siswa Takhta Perlawanan Putra Sinawang Teguh Bharata Adji Tri Wahyudi Trisha NurHalisha Tyas, Intan Kinanthi Damarin Wawan Joko Pranoto Wawan Joko Pranoto Wisnu Ismunandar Zayni Zayni