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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal F. Teknik : RESULTAN Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma Jurnal Ilmiah FIFO Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Jurnal Mahasiswa Bina Insani Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Komtika (Komputasi dan Informatika) IKRA-ITH EKONOMIKA Jurnal ICT : Information Communication & Technology JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Kajian Ilmiah Jurnal Sistem Informasi Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Journal of Students‘ Research in Computer Science (JSRCS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Journal of Computer Science Contributions (Jucosco) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Komtika (Komputasi dan Informatika) Journal of Intelligent Systems for Community Development (JISCoDe)
Herlawati Herlawati
Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Other

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Fine-Tuning Large Language Model (LLM) for Chatbot with Additional Data Sources Herlawati Herlawati; Rahmadya Trias Handayanto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10832

Abstract

Currently, Large Language Models (LLMs) are gaining popularity in implementation and research, with numerous open-source models available for use. One notable example is the AI-powered chat application, which leverages pre-trained LLMs to provide accurate and relevant information to users. By utilizing fine-tuning technology, this model can be tailored to specific student registration data, making it easier for prospective students to access the necessary information. Research findings indicate that this model achieves high accuracy in providing answers based on the inputted information. One of its advantages is its ability to generate training data through a Llama-based chat application, resulting in a more interactive and engaging user experience.
Comparative Study of PCA, t-SNE, and UMAP for CNN Feature Representation of Image Classification Herlawati Herlawati; Rahmadya Trias Handayanto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11634

Abstract

Currently, the use of Deep Learning is widespread across various domains, with Convolutional Neural Networks (CNNs) as one of its main pioneers due to the principle of convolution. Recent methods continue to emerge with steadily increasing accuracy, in some cases approaching perfection. However, their implementation is often limited by the lack of sufficient computational resources in many environments. Moreover, the growing demand for explainable AI compels researchers to explore approaches that reveal the inner workings of deep learning models rather than treating them as mere black boxes. In this study, a simple CNN model is employed as a testbed for examining the feature extraction process through convolution, which is subsequently transformed into a user-friendly two-dimensional representation. The dataset used in this study is the Cats and Dogs dataset from Kaggle, which contains 25,000 labeled images equally distributed between the two classes. The dimensionality reduction methods utilized include Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The results demonstrate that UMAP achieves superior performance compared to PCA and t-SNE, with the highest silhouette score and a lower Davies–Bouldin index, indicating more compact and well-separated feature clusters.
Performance Evaluation of YOLOv8 and YOLOv11 for River Waste Detection Herlawati Herlawati; Rahmadya Trias Handayanto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12295

Abstract

River pollution caused by plastic waste requires an effective and automated monitoring solution. This study proposes an automated waste detection system in river environments by implementing and comparing two deep learning-based object detection models, YOLOv8 and YOLO11. The dataset used is the River Trash dataset (Version 3) from Roboflow, consisting of 415 original images augmented to 1,281 training and 367 validation images across five waste categories: non-plastic, plastic bag, plastic bottle, plastic others, and plastic wrapper sachet. Both models were trained under identical conditions — 10 epochs, image size 640×640, batch size 16, and AdamW optimizer — to ensure a fair comparison. Performance was evaluated using Precision, Recall, mAP@50, and mAP@50-95. Results show that YOLOv8 achieved higher detection accuracy with Precision 0.880, Recall 0.889, mAP@50 0.946, and mAP@50-95 0.942, outperforming YOLO11 which recorded 0.879, 0.800, 0.910, and 0.909 respectively. However, YOLO11 demonstrated greater efficiency with fewer parameters (2.59M vs 3.01M) and faster inference speed (249ms vs 265ms), making it more suitable for edge device deployment. These findings confirm that both models are capable of detecting plastic waste in complex river environments, with YOLOv8 recommended for accuracy-critical applications and YOLO11 for resource-constrained real-time monitoring systems.
Pengelompokan Penerima Bantuan Kerusakan Bangunan Akibat Bencana Alam di Jawa Barat Menggunakan Algoritma K-Means Rizky Maulana Arrasyid; Herlawati Herlawati
Journal of Students‘ Research in Computer Science Vol. 5 No. 1 (2024): Mei 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/c8btc203

Abstract

Disasters have a tremendous impact on society, one of the impacts of natural disasters is building damage, a province that is very prone to natural disasters is West Java province and results in a lot of building damage due to disasters, the solution to this disaster needs building assistance caused by natural disasters. In this study discusses the application of the K-Means Clustering algorithm for recipients of aid due to natural disasters, this study took data from West Java open data with a data set of house damage this data consists of 2012-2022 covering 27 districts / cities in West Java Province. This research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which has six stages. The results of the data processed using K-Means clustering are divided into 4 clusters, namely, the level of highly prioritized clusters (C0), the level of prioritized clusters (C1), the level of less prioritized clusters (C2), and the level of non-prioritized clusters (C3), In this study, clusters that are highly prioritized in receiving assistance are Bogor Regency, Bandung Regency, Cianjur Regency, Garut Regency, Sukabumi Regency, and Tasikmalaya Regency.
Metode Naïve Bayes dan Support Vector Machine untuk Mengolah Sentimen Ulasan dan Komentar di Platform Digital Herlawati; Dwi Budi Srisulistiowati; Syafira Cessa Agustin; Prilia Hashifah Syafina; Nida Rachmatin; Siti Setiawati
Journal of Students‘ Research in Computer Science Vol. 5 No. 2 (2024): November 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/dby15h32

Abstract

This study analyzes sentiment from user Reviews of the FLO app, Taman Mini Indonesia Indah (TMII), and public comments on infidelity cases on Instagram, using Naïve Bayes and Support Vector Machine (SVM) algorithms. FLO, an app that helps users track reproductive health, was analyzed based on 1,393 Reviews on Google Play Store. Of these, 796 Reviews expressed positive sentiment, while 597 were negative. Although both Naïve Bayes and SVM achieved an accuracy of 74%, SVM performed better in recall (74%) and precision (71%). For TMII Reviews, the analysis involved 1,616 Google Reviews, with 1,263 showing negative sentiment, indicating complaints about facilities and services, and 353 expressing positive sentiment. SVM outperformed Naïve Bayes, achieving an accuracy of 85% and an f1-score of 87%, compared to Naïve Bayes’ 82% accuracy and 83% f1-score. Additionally, the analysis of 1,200 public comments on Instagram accounts @lambe_turah and @awreceh.id revealed 918 negative comments and 282 positive ones. SVM once again demonstrated superior performance with an accuracy of 91%, precision of 87%, recall of 96%, and an f1-score of 92%, surpassing Naïve Bayes, which achieved an accuracy of 86%. These findings confirm that SVM is more effective for sentiment classification across various digital Platforms, including social issues and service evaluations. The results can be applied to develop public opinion analysis systems that support strategic decision-making and enhance service quality based on user feedback.
Pendeteksian dan Klasifikasi Sampah pada Bank Sampah Berbasis Web Menggunakan YOLOv11 Marsyanda Salsa Nabila; Herlawati Herlawati; Agus Hidayat
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/r5me0z35

Abstract

The problem of poorly managed household waste management can increase the burden on the environment and reduce the effectiveness of recycling. Waste banks in general still rely on manual systems in sorting waste which is prone to errors and requires more labor. This research aims to develop a web-based waste detection and classification system using the You Only Look Once (YOLO) version 11 yolov11n (nano) method. The research method included downloading the main secondary dataset named R1 Test version 15 from the Roboflow Universe platform, collecting other secondary datasets from internet scraping and manual photography, which resulted in a total of 27,400 images of trash with nine different types, namely bottle, cans, cardboard, cup, foil, food, paper, paper_bag, and plastic.The results show that the yolov11n model is able to detect objects with sufficient accuracy and light computational resources by producing a precision value of 91,7%, recall of 89%, mAP50 of 93,2% and mAP50-95 of 75,8% in all classes. The best model results obtained are integrated into the web using the flask framework.
Analisis Sentimen Masyarakat Terhadap PHK di Indonesia Pada Twitter Menggunakan Naïve Bayes dan Support Vector Machine (SVM) Abdu Malik AlHakim; Prima Dina Atika; Herlawati Herlawati
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/96sfw544

Abstract

The phenomenon of layoffs in Indonesia has led to various public opinions, especially on social media. This research aims to analyze public sentiment on the layoff issue using data from Twitter, and compare the performance of two text classification algorithms, namely Naïve Bayes and Support Vector Machine. The Knowledge Discovery in Databases approach is used as the research framework, which includes the stages of data selection, text cleaning, transformation, classification, and evaluation. A total of 3,458 tweets were collected and processed through the pre-processing stage, then classified into positive and negative sentiments. Performance assessment was conducted with three scenarios of training and test data sharing: 80:20, 70:30, and 90:10. The results showed that Support Vector Machine gave the highest accuracy of 84.93% in the 90:10 scenario, compared to Naïve Bayes with 82.61% accuracy in the same scenario. Visualization through wordcloud was also used to strengthen the interpretation of dominant words in public opinion. The findings show that classification algorithms can be utilized to understand public perceptions of employment issues and support social data-based decision-making. This research can be further developed by expanding data coverage and evaluating more complex methods to improve classification accuracy.
Komparasi Kinerja ResNet50 dan MobileNetV2 pada Klasifikasi Citra Multi-Domain Herlawati, Herlawati; Hendharsetiawan, Andy Achmad; Priatna, Wowon; Dzulqiyana, Afina Putri; Rahmadanti, Regita Ari
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.16926

Abstract

This study aims to compare the performance of the Convolutional Neural Network (CNN) architectures ResNet50 and MobileNetV2 in multi-domain image classification, namely herbal leaf images and facial skin undertone images. The herbal plant dataset consists of 11 classes of leaf images, while the skin undertone dataset includes warm, cool, and neutral categories obtained from secondary and primary data sources. The research stages include image preprocessing, model training using transfer learning, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix. The results show that ResNet50 achieved better performance than MobileNetV2 in both classification domains. In herbal plant classification, ResNet50 achieved an accuracy of 95.00%, while MobileNetV2 obtained 94.09%. In facial skin undertone classification, ResNet50 achieved an accuracy of 89.3% and a weighted F1-score of 0.893, whereas MobileNetV2 achieved an accuracy of 68.3% and a weighted F1-score of 0.684. These findings indicate that ResNet50 is more effective and stable than MobileNetV2 for multi-domain image classification.
Penyuluhan Dan Pelatihan Keamanan Data Digital Bagi Masyarakat Dari Ancaman Kejahatan Cyber Herlawati; Rafly Fandiansyah; Mirza Cahya Ningrum; Caroline Julyana Magdalena; Muhammad Gymnastiar; Naufal Eka Wicaksono; Naufal Arif Fadilah; Raka Rismayana; Muhammad Reinaldy Santoso; Putra Aldi Purnama; Pahrizal Pahrizal; Nurcholis Nurcholis
Journal Of Computer Science Contributions (JUCOSCO) Vol. 6 No. 1 (2025): Januari 2026
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/va67rw83

Abstract

The rapid development of information technology has led to increased use of digital media by the community, which has not been accompanied by adequate understanding of digital data security. This condition makes the community vulnerable to various cybercrime threats such as online fraud, personal data theft, phishing, and the spread of hoaxes. This community service activity aims to improve the digital literacy and awareness of residents of Taman Kebayoran Housing Area RW 013, Setiamekar Village, Bekasi Regency, regarding the importance of digital data security through counseling and training programs. The implementation methods include initial socialization, face to face counseling, digital data security training, and the development of a Housing Profile Website as an official and verified information medium. The results show an increase in community understanding of types of cyber threats, methods to identify false information, and preventive measures against digital fraud. In addition, the existence of the housing profile website is considered effective in supporting information transparency and strengthening communication between community administrators and residents. The originality of this activity lies in the integration of community based cybersecurity education with the development of a local digital platform that functions as a literacy and official information medium. This program is expected to serve as a sustainable community service model in building a safe and responsible digital ecosystem at the neighborhood level.
Peningkatan Literasi Artificial Intelligence untuk Mendukung Belajar dan Produktivitas melalui Webinar Nasional Kolaborasi Perguruan Tinggi Herlawati; Rahmadya Trias Handayanto; Rakhmat Purnomo; Anindita Septiarini
Journal Of Computer Science Contributions (JUCOSCO) Vol 6 No 2 (2026): Juli 2026
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/5cb89a62

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed education and professional practices. However, maximizing the benefits of AI requires adequate digital literacy to ensure its effective, ethical, and responsible use. This community engagement program aimed to improve Artificial Intelligence literacy for learning and productivity through a National Collaborative Webinar involving multiple higher education institutions. The webinar was conducted online via Zoom Meeting on May 23, 2026, involving 345 participants from universities, schools, and other institutions across eight provinces in Indonesia. The implementation consisted of planning, material development, webinar promotion, pre-test, interactive presentations, and hands-on practice using AI applications such as ChatGPT, Claude, and Mendeley AI, followed by a post-test and participant satisfaction evaluation. The results demonstrated that the webinar successfully reached participants from diverse professional and institutional backgrounds. The comparison between pre-test and post-test results indicated improvements in most evaluation indicators, particularly participants’ intention to continuously use AI in learning and daily work, which increased from 35.7% to 41.9%. Participants’ ability to critically evaluate AI-generated outputs also increased from 43.2% to 44.8%, while the perception that AI improves task efficiency increased from 46.4% to 47.0%. These findings indicate that a collaborative national webinar is an effective community engagement approach to enhancing AI literacy while encouraging the productive and responsible adoption of AI technologies in education and professional activities.
Co-Authors A.A. Ketut Agung Cahyawan W Abd Rohman Abdu Malik AlHakim Abdul Kholis Acah Acah Achmad Noe’man Achmad Wira Wiguna Adam Adam Adam Fajariansyah Adi Muhajirin Adi Supriyatna admin admin Aera Santiana Afina Putri Dzulqiyana Agus Hidayat Agus Hidayat Ajie Prasetya Ajif Yunizar Pratama Yusuf Al Ihsan Fauzi Ardilla Andy Achmad Hendhar Setiawan Andy Achmad Hendharsetiawan Andy Achmad Hendharsetiawan Andy Achmad Hendharsetiawan Anggaini, Meri Anindita Septiarini, Anindita Anis Athifah Anisa Feby Yana Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anita Setyowati Srie Gunarti Anton Anton Ardiansyah, Muhamad Asmoro Bangun Priambodo Atika , Prima Dina Ayu Afidarisa Rahma Bangga Tua Siregar Bayu Andriansyah Ben Rahman Beno Aditya Sanusi Beno Aditya Sanusi Benrahman Bertnardo Mario Uskono Bhagaskara Farhan Wiguna Binu Nuryadi Budi Santoso Bunga Pratiwi Caroline Julyana Magdalena Christhover , Robbie Dadan Irwan Dani Dani Daniel Jhon Rosinton Hutauruk Desi Puspasari Diah Putri Ramadhani Diah Putri Ramadhani Diah Putri Ramadhani Dicki Rizki Amarullah Didik Setiyadi Dinda Mutiara Hanum Dwi Budi Santoso Dwi Budi Srisulistiowati Dzulqiyana, Afina Putri Eka Puspita Sari Eka Suryani Pratiwi Ekawati, Inna Endang Retnoningsih Erene Gernaria Sihombing, Erene Gernaria Ervan Dwi Kurniawan Fachrullyanta Adi Saputra Fachrullyanta Adi Saputra Fahrika, Andi Ika Faisal Adi Saputra Fata Nidaul Khasanah Feni Meilan Tasiba Firyal Rosiana Dita Frieyadie Galih Apriansha Pradana Gedhe Hilman Wakhid Gilby Lionska Wenas Handry Hartino Haris, Syamsul Alam Harviansyah, Muhammad Haryono Haryono Haryono Hendharsetiawan , Andy Achmad Hendharsetiawan, Andy Achmad Heri Prabowo Hero Suhartono Hero Suhartono, Hero Hutauruk , Daniel Jhon Rosinton I Komang Arya Trisumeikra Icah Fitri Yani Ikhsan Dwikurniawan Ikhsan Dwikurniawan Intan Cahya Syahfitri Intan Cahya Syahfitri Ira Wardani Irham Cahya Nugraha Irwan Raharja Ivan Nur Firdaus Izdihar, Zalfa Jaja Jaja Jaja Jaja Joko Dwi Hartanto Juandika Shevani Julaiwa, Siti Hawa Karnita Afnisari, Karnita Krisendo Setiawan Kukuh Dwi Prasetyo Kurniawan, Ervan Dwi Kustanto , Prio Ladyana Suciani Syafitri Laila Salsabilla Hanifa Lubis, Riski Aditya Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah Maimunah Malikus Sumadyo Marsyanda Salsa Nabila Mayora Lolly Ishimora Media Anugerah Ayu, Media Anugerah Merza Dheo Prakoso Mirza Cahya Ningrum Mochamad Galih Pradipta Muhamad Ardiansyah Muhammad Gymnastiar Muhammad Harviansyah Muhammad Muharrom Muhammad Reinaldy Santoso Muhammad Riky Sudrajat Muhammad Zidan Al Faiq Nabila Ramadhani Sari Naufal Arif Fadilah Naufal Eka Wicaksono Nida Rachmatin Nita Merlina Nita Merlina, Nita Nitin Kumar Tripathi Nitin Kumar Tripathi Noer Hikmah Novaldi Nur Pratama Novianto, Krisna Nunung Hidayatun Nur Amanda Pratiwi Nurchayati Nurchayati Nurcholis Nurcholis Oriza Sativa Dinauni Silaen Pahrizal Pahrizal Popy Purnamasari Wahid Suyitno Pradana , Galih Apriansha Pramod Kumar Priatna , Wowon Prihatin, Sandy Satyo Prilia Hashifah Syafina Prima Dina Atika Purnomo, Rakhmat Purnomo, Rakhmat Purwanti, Santi Putra Aldi Purnama Rafika Sari RAFIKA SARI Rafly Fandiansyah Rahmadanti, Regita Ari Rahmadya Trias Handayanto Rahmadya Trias Handayanto Rahmadya Trias Handayanto rahmadya trias handayanto Rahmadya Trias Handayanto Rahmadya Trias Handayanto Raihan Nurfaidzi Raka Rismayana Rakhmat Purnomo Rakhmat Purnomo Ramadhan, Sahara Ramadhani, Diah Putri Rasim Rasim Rejeki , Sri Retno Nugroho Whidhiasih Retno Sari Riska Utami Dewi Riski Aditya Lubis Rizki Aulianita, Rizki Rizky Maulana Arrasyid Robbie Christhover Robertus Suraji Rosliana, Siti Rusdiansyah Rusdiansyah Sahara Ramadhan Salwa Nabiila Pramuhesti Samsiana , Seta Sandy Satyo Prihatin Sanusi, Beno Aditya Saputra , Faisal Adi Saputra, Fachrullyanta Adi Sari , Rafika SATRIYAS ILYAS Septi Eka Hardyana Septia, Dwi Yoga Seta Samsiana Seta Samsiana Seta Samsiana Seta Samsiana Seta Samsiana Setyowati Srie Gunarti, Anita Shadriyah , Shadriyah Silaen, Oriza Sativa Dinauni Siti Hawa Julaiwa Siti Masripah Siti Rosliana Siti Setiawati Sohee Minsun Kim Solikin Solikin Solikin Solikin Sri Rejeki Sri Sureni Sugeng Murdowo Sugiyatno , Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sultan Ahmad Rizki Badani Sunandar Sunandar Syadhaffa Gedriyansah Syafira Cessa Agustin Syahbaniar Rofiah Syahfitri, Intan Cahya Syamsul Alam Haris Tambun, Jerisman Jhon Wesli Tata Arya Cahyaaty Teddy Mantoro Tia Monisya Afriyanti Tumbur Togu Tyastuti Sri Lestari Tyastuti Sri Lestari Umi Salamah Umi Salamah Wida Prima Mustika Yana, Anisa Feby Yessi Rahmawati Yugo Bhekti Utomo Yusuf, Ajif Yunizar Pratama