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All Journal Techno.Com: Jurnal Teknologi Informasi Telematika Scientific Journal of Informatics Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK POLBENG INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem informasi dan informatika (SIMIKA) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science International Journal of Advances in Artificial Intelligence and Machine Learning
Efrizoni, Lusiana
Program Studi Teknik Informatika, STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Perbandingan Algoritma K-Nearest Neighbors dan Random Forest untuk Rekomendasi Gaya Hidup Sehat dalam Mencegah Penyakit Jantung: Comparison of K-Nearest Neighbors and Random Forest Algorithms for Recommendations for a Healthy Lifestyle in Prevent Heart Disease Sahelvi, Elza; Cikita, Putri; Sapitri, Riska Mela; Rahmaddeni, Rahmaddeni; Efrizoni, Lusiana
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1972

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian yang disebabkan oleh faktor gaya hidup tidak sehat. Untuk mengatasi permasalahan ini, penelitian ini membandingkan algoritma K-Nearest Neighbors (KNN) dan Random Forest (RF) dalam memberikan rekomendasi gaya hidup sehat guna mencegah penyakit jantung. Dataset yang digunakan terdiri dari 1.025 entri dengan 14 fitur, yang telah melalui tahap preprocessing, termasuk normalisasi, seleksi fitur, dan pembagian data 80:20 serta 70:30. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Random Forest memiliki akurasi lebih tinggi (99% pada skenario 80:20 dan 98% pada skenario 70:30) dibandingkan KNN (83% dan 86%), serta lebih stabil dalam mengklasifikasikan risiko penyakit jantung. Analisis fitur menunjukkan bahwa Chest Pain Type (CP) atau nyeri dada merupakan faktor paling berpengaruh. Berdasarkan hasil ini, direkomendasikan pola makan sehat, aktivitas fisik teratur, manajemen stres, serta pemeriksaan kesehatan rutin. Kesimpulannya, Random Forest lebih efektif dalam sistem rekomendasi gaya hidup sehat, dan penelitian selanjutnya dapat menggunakan dataset lebih besar dengan variabel tambahan guna meningkatkan akurasi prediksi.
Model Prediksi Dampak Perubahan Iklim pada Ketahanan Pangan Menggunakan Algoritma Support Vector Machine and K-Nearest Neighbors: Prediction Model for the Impact of Climate Change on Food Security Using the Support Vector Machine and K-Nearest Neighbors Algorithms Sari, Devi Puspita; Risman, Risman; Maulana, Fitra; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1975

Abstract

Perubahan iklim memberikan dampak signifikan terhadap ketahanan pangan global, terutama di wilayah yang sangat bergantung pada sektor agrikultur. Fenomena seperti curah hujan ekstrem, kenaikan suhu, dan perubahan pola angin telah memengaruhi produktivitas pertanian secara signifikan. Urgensi penelitian ini terletak pada pentingnya pengembangan model prediktif berbasis data untuk mengantisipasi dampak perubahan iklim terhadap ketahanan pangan, sehingga strategi adaptasi dapat dirancang secara tepat oleh pembuat kebijakan. Penelitian ini bertujuan mengembangkan model prediksi dampak perubahan iklim terhadap ketahanan pangan dengan memanfaatkan algoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN). Dataset yang digunakan meliputi data meteorologi harian, seperti curah hujan (precipitation), suhu maksimum (temp_max), suhu minimum (temp_min), dan kecepatan angin (wind), yang diperoleh dari Kaggle (Seattle weather). Model SVM diterapkan untuk menangkap hubungan non-linear antara parameter iklim dengan indikator ketahanan pangan, sedangkan KNN digunakan untuk menganalisis pola serupa pada data historis. Hasil penelitian menunjukkan bahwa SVM memiliki akurasi prediksi sebesar 78%, lebih unggul dibandingkan KNN yang mencapai akurasi 74%. Temuan ini membuktikan bahwa SVM lebih efektif dalam memodelkan keterkaitan antara variabel iklim dan ketahanan pangan. Dengan demikian, penelitian ini berhasil mencapai tujuannya dan memberikan kontribusi penting dalam pengembangan sistem prediksi berbasis machine learning untuk mendukung kebijakan pangan yang adaptif terhadap perubahan iklim.
Optimization of Customer Segmentation in the Retail Industry Using the K-Medoid Algorithm Agustin, Endy Wulan; Uthami, Kurnia; Ulfa, Arvan Izzatul; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1977

Abstract

The retail industry faces significant challenges in understanding increasingly complex customer behavior due to massive data growth. One major obstacle is suboptimal customer segmentation, leading to ineffective marketing strategies. This study aims to optimize customer segmentation by implementing the K-Medoid algorithm, which excels in handling outliers and producing more stable clusters compared to K-Means. The dataset consists of over 10,000 customer transactions from a major retail company in Indonesia. The research process includes data collection and preprocessing, K-Medoid algorithm implementation, and performance evaluation using the silhouette score. The results indicate that the K-Medoid algorithm achieves more accurate customer segmentation, with a silhouette score of 0.39. The generated clusters exhibit greater homogeneity, enabling companies to design more targeted marketing strategies, such as specific discount offers and tailored loyalty programs. Based on these findings, the K-Medoid algorithm is recommended to enhance customer management effectiveness in the retail industry. This study contributes to selecting a more suitable algorithm for customer segmentation in the era of big data and opens opportunities for further exploration of hybrid algorithms and additional evaluation metrics.
Analisis Faktor-Faktor yang Mempengaruhi Engagement Video di Platform TikTok Menggunakan Multiple Linear Regression: Analysis of Factors that Influence Video Engagement on the TikTok Platform Using the Multiple Linear Regression Algorithm Sapina, Nur; Nanda, Annisa; Arifin, Muhammad Amirul; Rahmaddeni, Rahmaddeni; Efrizoni, Lusiana
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1987

Abstract

TikTok telah berkembang menjadi salah satu platform interaksi digital terkenal secara luas di seluruh dunia, yang memiliki lebih dari satu miliar orang pengguna aktif. Namun, sebagian video di TikTok memperoleh tingkat engagement yang tinggi meskipun menggunakan pendekatan konten yang serupa. Riset ini dimaksudkan untuk menelusuri unsur-unsur yang memberikan pengaruh terhadap engagement video di TikTok dengan menerapkan algoritma Regresi Linear Berganda. Variabel yang dianalisis meliputi durasi video, jumlah tayangan, komentar, like, share, dan download. Setelah melalui tahap preprocessing data, seleksi fitur, dan pengujian asumsi regresi, ditemukan bahwa video_like_count, video_share_count, dan video_download_count memiliki pengaruh paling signifikan terhadap jumlah tayangan. Hasil evaluasi model membuktikan bahwa model regresi menujukkan kinerja prediktif yang sangat baik, dengan nilai R² Squared sebesar 0,978, RMSE sebesar 0,0742, dan MSE sebesar 0,0055. Riset ini memberikan gambaran praktis kepada konten kreator dan konten marketing dalam merancang produksi konten yang lebih optimal. Model prediksi ini juga dapat dimanfaatkan untuk memperkirakan potensi engagement suatu video sebelum dipublikasikan.
Model Klasifikasi IPK Mahasiswa Menggunakan Algoritma Decision Tree dan Random Forest Berbasis Feature Engineering Firman, Muhammad Aditya; Djamalilleil, Said Azka Fauzan; Zega, Wilman; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12384

Abstract

Indeks Prestasi Kumulatif (IPK) merupakan indikator utama dalam menilai keberhasilan akademik mahasiswa. Berbagai faktor, termasuk kesehatan mental dan fisik, berkontribusi terhadap pencapaian ini. Penelitian ini bertujuan untuk membangun model prediksi IPK menggunakan algoritma Decision Tree dan Random Forest berbasis Feature Engineering. Proses feature engineering mencakup feature selection untuk memilih fitur paling relevan, diikuti oleh feature extraction yang menyederhanakan fitur menjadi dua kategori utama: kesehatan mental dan fisik. Data diperoleh melalui survei terhadap 7.022 mahasiswa dari berbagai universitas luar negeri, mencakup faktor usia, jurusan, tingkat stres, kecemasan, serta pola tidur, aktivitas fisik dan lain sebagainya. Model prediksi dikembangkan menggunakan Decision Tree dan Random Forest, dengan evaluasi akurasi kedua algoritma. Hasil penelitian menunjukkan bahwa Random Forest memiliki akurasi lebih tinggi dibandingkan Decision Tree. Faktor kesehatan mental, terutama tingkat stres, memiliki pengaruh signifikan terhadap prediksi IPK, disusul oleh pola tidur. Studi ini menegaskan bahwa pemantauan kesehatan mental dan fisik mahasiswa dapat meningkatkan pencapaian akademik. Temuan ini diharapkan dapat membantu institusi pendidikan dalam merancang strategi dukungan akademik berbasis kesehatan mahasiswa.   Kata Kunci: Indeks Prestasi Kumulatif (IPK), Machine Learning, Decision Tree, Random Forest, Feature Engineering
SISTEM REKOMENDASI VIDEO GAME BERBASIS USIA SEBAGAI ALAT PENGAWASAN ORANG TUA DI PLATFORM STEAM MENGGUNAKAN CONTENT-BASED FILTERING Oktavianda, Oktavianda; Efrizoni, Lusiana; Fatdha, Eiva; Asnal, Hadi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/2wj54451

Abstract

Video recreations are a prevalent shape of amusement, particularly among children. In any case, numerous parents in Indonesia still need understanding of age appraisals for video recreations, driving to less viable supervision. This could uncover children to unseemly substance. This think about points to create an age-based video amusement suggestion framework utilizing the Content-Based Filtering strategy on the Steam dataset. The framework is planned to help guardians in selecting recreations suitable for their children. Evaluation results show the model performs very well, achieving a precision of 0.98 and a recall of 1.00. Additionally, the model records a Mean Absolute Error (MAE) of 0.469236, Mean Squared Error (MSE) of 6.440935, and Root Mean Squared Error (RMSE) of 2.537900. These findings highlight how well the system filters and suggests age-appropriate video games, assisting parents in better monitoring their kids' gaming habits.
Deep Learning Innovations in Fingerprint Recognition: A Comparative Study of Model Efficiencies Efrizoni, Lusiana; Armoogum , Sheeba; Zakaria , Mohd Zaki
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 1 No. 1 (2024): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v1i1.294

Abstract

Fingerprint recognition technology is integral to biometric security systems, providing secure and reliable identification through unique human fingerprint patterns. However, challenges such as low contrast, high intra-class variability, and partial fingerprints often compromise the efficiency and accuracy of traditional recognition systems. This research addresses these challenges by employing advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), to enhance fingerprint recognition performance. We propose a methodological approach that leverages state-of-the-art CNN architectures tailored to capture intricate fingerprint details. The study utilizes the Sokoto Coventry Fingerprint Dataset (SOCOFing), which includes diverse fingerprint types and synthetic alterations to evaluate model performance under realistic conditions. Through a comparative analysis of various CNN configurations, we assessed the models based on efficiency and accuracy, using metrics such as accuracy, precision, recall, and F1-score. Our experimental results demonstrate significant improvements in fingerprint recognition capabilities. The optimized CNN model achieved an accuracy of 98.61%, a precision of 97.12%, a recall of 97.46%, and an F1-score of 97.29%. These results validate the effectiveness of CNNs in handling complex biometric data and underscore their potential to enhance the reliability and security of fingerprint recognition systems. The study concludes that deep learning, through the use of CNNs, offers a powerful solution to the limitations of traditional fingerprint recognition techniques. This will pave the way for more sophisticated and accurate biometric security systems in practical applications. The research findings contribute to ongoing advancements in neural network architectures, enhancing their applicability in increasingly automated and data-driven security environments.
AI and the Optimization of Product Placement: Enhancing Sales through Strategic Positioning kasim, Shahreen; Zakaria, Mohd Zaki; Efrizoni, Lusiana; Fadly, Fadly
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.381

Abstract

This study aims to analyze the impact of strategic product placement and promotion strategies using the Customer's Purchase Behavior Dataset. The study utilized a controlled experimental design, wherein trial stores were matched with control stores based on pre-trial performance metrics, including total sales and customer demographics. A detailed exploratory data analysis (EDA) was conducted to segment customers based on life-stage and purchasing behaviour. Additionally, a t-Test was performed to determine whether price sensitivity and purchasing patterns differed significantly between mainstream, budget, and premium customer segments. The results indicate that trial stores implementing strategic initiatives experienced a measurable uplift in sales compared to their control counterparts. Young and mid-age singles and couples in the mainstream category were found to be more willing to pay a premium for chips, whereas families tended to purchase in bulk. The t-test confirmed statistically significant differences in purchasing behaviour across customer segments. The findings suggest that a data-driven, segment-specific marketing approach can optimise retail performance by aligning promotions and pricing with the behavioural tendencies of different consumer groups. This study demonstrates that well-targeted strategic retail initiatives can significantly improve sales performance. The insights derived from this research provide retailers with actionable strategies for tailoring product placement and promotions to maximise customer engagement. Future work should incorporate machine learning techniques to refine predictive models for real-time decision-making in retail marketing.
Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Lestari, Tri Putri; Efrizoni, Lusiana; Handayani, Nadya Satya; Andhika, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.
Adaptive Neural Collaborative Filtering with Textual Review Integration for Enhanced User Experience in Digital Platforms Efrizoni, Lusiana; Ali, Edwar; Asnal, Hadi; Junadhi, Junadhi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.944

Abstract

This research proposes a hybrid rating prediction model that integrates Neural Collaborative Filtering (NCF), Long Short-Term Memory (LSTM), and semantic analysis through Natural Language Processing (NLP) to enhance recommendation accuracy. The main objective is to improve alignment between system predictions and actual user preferences by leveraging multi-source information from the Amazon Movies and TV dataset, which includes explicit user–item ratings and textual reviews. The core idea is to combine three complementary processing paths—(1) user–item interaction modeling via NCF, (2) temporal dynamics capture through LSTM, and (3) semantic understanding of reviews using NLP—into a unified deep learning-based adaptive architecture. Experimental evaluation demonstrates that this multi-input approach outperforms the baseline collaborative filtering model, with the Mean Absolute Error (MAE) reduced from 1.3201 to 1.2817 (a 2.91% improvement) and the Mean Squared Error (MSE) reduced from 2.2315 to 2.1894 (a 1.89% improvement). Training metrics visualization further shows a stable convergence pattern, with the MAE gap between training and validation consistently below 0.03, indicating minimal overfitting. The findings confirm that integrating cross-dimensional signals significantly enhances predictive performance and can contribute to increased user satisfaction and engagement in recommendation platforms. The novelty of this work lies in the simultaneous integration of interaction, temporal, and semantic dimensions into a single adaptive recommendation framework, a configuration not jointly explored in prior studies. Moreover, the flexible architecture enables adaptation to other domains such as e-commerce, music, or online learning, broadening its practical applicability.
Co-Authors -, Dwi Haryono Afrinanda, Rizky Agung Marinda Agus Tri Nurhuda Agustin Agustin Agustin Agustin Agustin Agustin, Endy Wulan Ahmad - Fauzan Ahmad Fauzan Ahmad Rizali Anam, M Khairul Andhika, Imam Anthony Anggrawan Anugraha, Yoga Safitra Aprilia, Fanesa Arifin, Muhammad Amirul Armoogum , Sheeba Aulia, Rahma Azhari, Zahra Cikita, Putri Dadynata, Eric Dea Safitri Deni, Rahmad Devi Puspita Sari, Devi Puspita Dhini Septhya Djamalilleil, Said Azka Fauzan Edwar Ali Erlinda, Susi Ermy Pily, Annisa Khoirala ester nababan Fadly Fadly Farhan Pratama Farida Try Puspa Siregar Fatdha, Eiva Febrianda Putra Filza Izzati Finanta Okmayura Firdaus, Muhammad Bambang Firman, Muhammad Aditya Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Hafsah Fulaila Tahiyat Handayani, Nadya Satya Haviluddin Haviluddin Helda Yenni, Helda Hidaya Spitri Iftar Ramadhan Ihsan, Raja Muhammad Ike Yunia Pasa Irwanda Syahputra Julianti, Nadea Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Karpen Kartina Diah K. W. Kharisma Rahayu Koko Harianto Lathifah, Lathifah Lestari, Fika Ayu Lili Marlia M. Azzuhri Dinata M. Irpan Marhadi, Nanda Maulana, Fitra Melva Suryani Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Syaifullah MUHAMMAD TAJUDDIN Munawir Munawir Muslim Muslim Nanda, Annisa Nasution , Zikri Hardyan Nurul fadillah, Nurul Oktavianda Oktavianda, Oktavianda Purnama, Muhammad Adji Putra, Febrianda Putri, Adinda Dwi Putri, Siti Faradila R. Guntur Surya Yuwana - Rahmaddeni Rahmaddeni Rahmaddeni, - Rahmiati Rahmiati Rais Amin Raja Muhammad Ihsan Ramadhani, Jilang Rati Rahmadani Ratna Andini Husen Revaldo, Bagus Tri Riadhil Jannah Rini Yanti, Rini Risky Harahap Risman Risman Rizki Astuti Rometdo Muzawi, Rometdo Sahelvi, Elza Salsabila Rabbani Sapina, Nur Sapitri, Riska Mela Sari, Atalya Kurnia Sarjon Defit Sarjon Defit Setiawan , Andri Shahreen Kasim, Shahreen Sholekhah, Fitriana Sularno Supian, Acuan Susandri, Susandri Susanti, Susanti Susi Erlinda Syahrul Imardi Syarifuddin Elmi Tahiyat, Hafsah Fulaila Tashid Tawa Bagus, Wahyu Torkis Nasution Tri Putri Lestari, Tri Putri Tri Revaldo, Bagus Triyani Arita Fitri Ulfa, Arvan Izzatul Unang Rio Uthami, Kurnia Vindi Fitria Wirta Agustin Wirta Wirta Yanti, Rini Yoyon Efendi Yulli Zulianda Zahra Azhari Zakaria , Mohd Zaki Zakaria, Mohd Zaki Zega, Wilman Zikri Hadryan nst Zulafwan Zuriatul Khairi Zuriatul Khairi