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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) ELKHA : Jurnal Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Jurnal Transformatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Sisforma: Journal of Information Systems JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Journal of Information System Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Wikrama Parahita : Jurnal Pengabdian Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Perangkat Lunak JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET) J-SAKTI (Jurnal Sains Komputer dan Informatika) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Jurnal Maklumatika Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Journal of Computing Theories and Applications Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Journal of Future Artificial Intelligence and Technologies Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
Ajib Susanto
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography Susanto, Ajib; Sinaga, Daurat; Mulyono, Ibnu Utomo Wahyu
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.9561

Abstract

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.
SISTEM MONITORING SUHU DAN KELEMBABAN KANDANG AYAM BERBASIS INTERNET OF THINGS (IOT) Novita Kurnia Ningrum; Kusuma, Tiara Widya; Wahyu Mulyono, Ibnu Utomo; Susanto, Ajib; Kusumawati, Yupie
Elkom: Jurnal Elektronika dan Komputer Vol. 16 No. 2 (2023): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v16i2.1153

Abstract

Broiler chickens are livestock whose growth is influenced by environmental temperature. The temperature of the chicken coop that is not suitable can affect the decrease in productivity and cause death in broiler chickens, so that the temperature setting of the cage must be considered. The design of this temperature and humidity monitoring system uses a nodemcu ESP8266 microcontroller and an arduino uno. If the measured temperature exceeds the set temperature limit, the system will send an SMS to the smartphone so that the cage officer can take appropriate action.
A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19 Susanto, Ajib; Sari, Christy Atika; Rachmawanto, Eko Hari; Mulyono, Ibnu Utomo Wahyu; Mohd Yaacob, Noorayisahbe
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47305

Abstract

Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model.Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%.Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.
Implementasi dan Evaluasi Model Machine Learning untuk Optimalisasi Prediksi Penjualan Produk Kue Kering Hilmi, Muhammad Abror Auliya; Susanto, Ajib
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8657

Abstract

The modern retail sector, such as Transmart, faces difficulties in maintaining stable sales performance due to changes in consumer behavior, variations in product types, and differing store characteristics. To address this issue, this study proposes the use of the Extreme Gradient Boosting (XGBoost) machine learning algorithm to predict retail product sales volumes based on historical data from 2024–2025. The research utilizes the CRISP-DM framework, which consists of the following stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data cleaning and preprocessing processes involve several steps such as data cleaning, label encoding, feature selection, and data splitting with an 80:20 ratio. The model is further evaluated using the Mean Absolute Error (MAE) and the coefficient of determination (R²) metrics to assess prediction accuracy. The findings indicate that XGBoost is capable of effectively capturing sales patterns and generating accurate predictions to support decision-making strategies in the retail sector, particularly in stock planning and sales optimization. Therefore, the implementation of this data-driven predictive approach is expected to assist companies in enhancing operational management as well as improving competitiveness in the market.
IT SERVICE MANAGEMENT FOR BUSINESS PROCESS OF STAFF ARCHIVE DIGITALIZATION USING ITIL 3TH VERSION FRAMEWORK (CASE STUDY: BKPP OF SEMARANG CITY) Yupie Kusumawati; Anggia Rizkika Hanin; Novita Kurnia Ningrum; Ibnu Utomo Wahyu Mulyono; Ajib Susanto; Karis Widyatmoko
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 1 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i1.187

Abstract

The role of archivist in an agency is as needed as in the Badan Kepegawaian, Pendidikan dan Pelatihan Kota Semarang (BKPP of semarang).Badan Kepegawaian, Pendidikan dan Pelatihan Kota Semarang currently has digitized archive services Employment is e-file service. The problem we found today is service the e-file is only used as a data archive and has not yet supported the business process resources. Another problem found on e-file services is not yet available service contact and lack of human resources. To support the business process then this research will design the management of it services on digitalization of employment archives. On this rre-design, e-file services will be linked to other IT services such as SISDM and E-Fungsional. This research uses the framework ITIL 3th Version domain service design with the sub domain service design package and service catalogue for document creation of the entire sequence of design. On this research there are also multiple support services like a service in contact, hours level services, risk identification, enhanced resources and structures the organization proposal so that current e-file services can support business processes digitalization of employment archives on the BKPP Kota Semarang
Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition Rahadian Kristiyanto Rachman; De Rosal Ignatius Moses Setiadi; Ajib Susanto; Kristiawan Nugroho; Hussain Md Mehedul Islam
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10459

Abstract

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.
Digitalisasi dan Optimasi Pelayanan Administrasi Kependudukan dalam Mendukung Go Digital dan Smart City di Kelurahan Pedurungan Karis Widyatmoko; Abdussalam Abdussalam; Lekso Budi Handoko; Ajib Susanto; Ibnu Utomo Wahyu Mulyono
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 2 (2026): MEI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i2.3234

Abstract

Kegiatan pengabdian masyarakat ini dilatarbelakangi oleh pesatnya perkembangan teknologi digital dan dukungan terhadap masterplan Smart City Kota Semarang 2021-2026. Masalah utama yang ditemukan di Kelurahan Pedurungan Tengah adalah proses administrasi kependudukan yang masih bersifat manual, sehingga memerlukan digitalisasi untuk meningkatkan efisiensi pelayanan publik. Metode yang digunakan dalam kegiatan ini meliputi tahap identifikasi dan observasi, perancangan serta pengembangan sistem, sosialisasi dan pelatihan kepada 50 warga, hingga tahap implementasi dan evaluasi. Hasil dari kegiatan ini adalah terwujudnya platform digital "RT/RW Online" melalui website resmi https://rt3rw16pedteng.my.id/users/home yang memungkinkan warga melakukan pengajuan surat secara mandiri. Kesimpulannya, implementasi sistem ini berhasil mengoptimalkan pelayanan publik dengan mengubah sistem manual menjadi digital yang lebih transparan dan cepat. Meskipun demikian, variasi tingkat literasi digital di antara warga tetap memerlukan pendampingan intensif sebagai langkah keberlanjutan program.Kata kunci: Administrasi Kependudukan, Digitalisasi, Pelayanan Publik, Pedurungan Tengah, Smart City.
Understanding Statistical and Temporal Representations for Large-Scale IoT DDoS Detection Through Ablation-Driven Analysis Daniel Nomolas Wicaksono; De Rosal Ignatius Moses Setiadi; Ajib Susanto; Imanuel Harkespan; Mohamad Afendee Mohamed; Aceng Sambas
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16126

Abstract

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.
Analisis Sentimen Twitter Terhadap Isu Royalti Lagu di Industri Musik Indonesia Menggunakan Naive Bayes dan Support Vector Machine Berbasis TF-IDF Alif Fadhil Wibowo; Ajib Susanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9842

Abstract

The development of digital platforms in Indonesia’s music industry has triggered various debates regarding the song royalty system, particularly those related to copyright and income distribution for songwriters. Public opinions on these issues are widely expressed through Twitter, making it a valuable data source for sentiment analysis. This study aims to analyze public sentiment toward song royalty issues in the Indonesian music industry and compare the performance of Multinomial Naive Bayes and Support Vector Machine (SVM) algorithms using TF-IDF weighting. This study contributes through the implementation of semi-manual labeling, the use of a stratified 5-fold cross-validation approach, and multi-metric evaluation to obtain more representative sentiment classification results on song royalty issues in Indonesian social media. The initial dataset was collected through Twitter scraping using keywords related to song royalties and music copyright. The data were then processed through preprocessing stages, including case folding, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using a semi-manual approach, involving lexicon-based pre-labeling followed by manual verification into three sentiment categories: positive, negative, and neutral. Model evaluation was performed using stratified 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm outperformed Multinomial Naive Bayes, achieving an accuracy of 93.21%, while Multinomial Naive Bayes obtained an accuracy of 82.53%. These findings demonstrate that SVM is more effective in handling high-dimensional textual data represented using TF-IDF for Indonesian sentiment analysis. This study is expected to provide insights into public perceptions regarding song royalty issues and serve as a reference for sentiment analysis applications on Indonesian social media data.
Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification Ajib Susanto; Christy Atika Sari; Hidayah Rahmalan; Mohamed A. S. Doheir
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.24928

Abstract

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.
Co-Authors - Wijanarto - Wijanarto -, Wijanarto -, Wijanarto Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abiyyi, Ryandhika Bintang Aceng Sambas Adrian Angga Pramono Afrizal Aziz Maulana Agus Winarno Agus Winarno, Agus Akhmad Rizaldy Ali Muqoddas Ali, Rabei Raad Alif Fadhil Wibowo Alviana Dina Putri Anak Agung Gede Sugianthara Anggia Rizkika Hanin Anggraeny, Tiara Antonio Ciputra Antonius Erick Handoyo Antonius Wibowo Aris Marjuni Atmojo, Cahyo Tri Bayu Wicaksono Briliantino Abhista Prabandanu Bustami, Sri Heri Cahyani, Anis Putma Carmelita, Bastiaans Jessica Christy Atika Sari Ciputra, Antonio D.R.I.M. Setiadi Daniel Nomolas Wicaksono De Rosal Ignatius Moses Setiadi Desi Purwanti Desi Purwanti Kusumaningrum Dian Kristiawan Nugroho Didik Hermanto Dimas Irawan Ihya’ Ulumuddin Doheir, Mohamed Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erwin Erwin Etika Kartikadarma Fakhriyan Nur Rofiq Farrel Athaillah Putra Febrian, Muhamad Rizky Fajar Fikri Budiman Fikri Budiman Galih Setyo Wibowo Gan, Hong-Seng Gilang Raharjito Haqikal, Hafidz Hayu Wikan Kinasih Hidayah Rahmalan Hilmi, Muhammad Abror Auliya Hussain Md Mehedul Islam Ibnu Gemaputra Ramadhan Ibnu Utomo Ibnu Utomo W.M. Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Ibnu Utomo WM Ihya Ulumuddin, Dimas Irawan Imam Kurniawan Imam Prayogo Pujiono Imanuel Harkespan Indra Kusuma Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Karis Widyatmoko Khafiizh Hastuti Krismawan, Andi Danang Kusuma, Tiara Widya Kusumawati, Yupie L. Budi Handoko Laksono, Enggar Adji Lalang Erawan Latifah Diah Kumalasari Lutfi Madiono Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mega Bintang Hatmi Moch Arief Soeleman Mochammad Lukman Mohamad Afendee Mohamed Mohamed A. S. Doheir Mohammad Arif Muttaqin Mohd Yaacob, Noorayisahbe Muhammad Atho’il Maula Muhammad Nur Haztinanto Mulyanto, Ibnu Utomo Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslih Muslih Muslih Muslih Muslih Nabiha Riandika, Muhammad Afiq Ningrum, Novita Kurnia Noorayisahbe Mohd. Yaacob Nova Rijati Novita Kurnia Ningrum Novita Kurnia Ningrum Nugroho, Widhi Bagus Ojugo, Arnold Adimabua Ozagastra Caluella Prambudi Ozagastra Caluella Prambudi Panjaitan, Yonathan Gani Panjaitan, Yonathan Gani Purwanto Purwanto Putri, Clara Edrea Evelyna Sony Raga Nufusula Rahadian Kristiyanto Rachman Raihan Yusuf Ramadhan, Aditya Wahyu Respatria, Nabila Maharani Rico Rian Alvian Rosyida, Ghaitsa Ardelia Sabilillah, Ferris Tita Saputra, The Manuel Eric Saraswati, Galuh Wilujeng Sarker, Md Kamruzzaman Sembiring, Rinawati Setiarso, Ichwan Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinar Setyawan Stefanus Santosa Sudaryanto Sudaryanto Sudaryanto Sudaryanto SUDARYANTO SUDARYANTO Sudaryanto Sudaryanto Suprayogi Suprayogi Teresa Enades Hari Setia Tiara Anggraeny Tiara Widya Kusuma Tri Wulandari Utomo W.M, Ibnu Utomo W.M, Ibnu Wellia Shinta Sari Widyatmoko Karis Wijanarto Wijanarto Wijanarto Wijanarto Wijanarto Wijanarto Yupie Kusumawati Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan Zuama, Leygian Reyhan