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All Journal Infotech Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JET (Journal of Electrical Technology) IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik dan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jurnal Sains dan Teknologi Community Engagement and Emergence Journal (CEEJ) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Darma Agung Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal Of Science, Technology & Management (IJSTM) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Media Informatika Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Fokus Manajemen Jurnal Minfo Polgan (JMP) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Nasional Teknologi Komputer Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Data Sciences Indonesia (DSI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Best Journal of Administration and Management Bulletin of Engineering Science, Technology and Industry
Sitorus, Zulham
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ANALISIS SENTIMEN TERHADAP ULASAN PENGGUNA APLIKASI RUMAH PENDIDIKAN DI PLAYSTORE MENGGUNAKAN ALGORITMA NAIVE BAYES Razaq, Abdul; Hidayat, Rahmat; Pasaribu, Ryan Fahreza; Sitorus, Zulham
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4935

Abstract

Abstract: The development of digital technology has led to the emergence of various educational applications that play an important role in supporting the teaching and learning process. One of the most widely used applications is Rumah Pendidikan, which provides a variety of online learning features. However, user perceptions and experiences of this application vary greatly, requiring a systematic analysis to determine public sentiment towards the application. This study aims to apply the Naive Bayes Classifier algorithm in analyzing user sentiment reviews of the Rumah Pendidikan application on the Google Playstore platform. The data, consisting of 284 reviews, was classified into three sentiment categories, namely positive, negative, and neutral. The analysis process included data cleaning, tokenization, stopword removal, and TF-IDF vectorization before classification. The results show that the Naive Bayes algorithm is capable of classifying sentiment with an accuracy of 78.95%, a precision value of 72%, a recall of 67%, and an F1-score of 70%. These findings indicate that the Naive Bayes approach is effective in identifying user sentiment towards the Rumah Pendidikan application. The findings of this analysis can be used by developers as a basis for consideration in improving the quality of the application and user satisfactioninthefuture.Keyword: Sentiment Analysis, Naive Bayes, Rumah Pendidikan Application, Google Play StoreAbstrak: Perkembangan teknologi digital telah mendorong munculnya berbagai aplikasi pendidikan yang berperan penting dalam mendukung proses belajar-mengajar. Salah satu aplikasi yang banyak digunakan adalah Rumah Pendidikan, yang menyediakan beragam fitur pembelajaran daring. Meskipun demikian, persepsi dan pengalaman pengguna terhadap aplikasi ini sangat beragam, sehingga diperlukan analisis yang sistematis untuk mengetahui sentimen masyarakat terhadap aplikasi tersebut. Penelitian ini bertujuan untuk menerapkan algoritma Naive Bayes Classifier dalam menganalisis sentimen ulasan pengguna terhadap aplikasi Rumah Pendidikan pada platform Google Playstore. Data yang terdiri atas 284 ulasan diklasifikasikan ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral. Proses analisis meliputi tahap data cleaning, tokenization, stopword removal, serta TF-IDF vectorization sebelum dilakukan klasifikasi. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu mengklasifikasikan sentimen dengan akurasi sebesar 78.95%, nilai precision sebesar 72%, recall sebesar 67%, dan F1-score sebesar 70%. Temuan ini menunjukkan bahwa pendekatan Naive Bayes mampu bekerja secara efektif dalam mengidentifikasi sentimen pengguna terhadap aplikasi Rumah Pendidikan. Temuan analisis tersebut dapat dimanfaatkan oleh pengembang sebagai dasar pertimbangan dalam meningkatkan kualitas aplikasi dan tingkat kepuasan pengguna dimasamendatang.Kata kunci: Analisis Sentimen, Naive Bayes, Rumah Pendidikan, Playstore
A Optimization of Sales Strategies and Inventory Forecasting for Processed Banana Products Utilizing the Conceptual Framework of Economic Efficiency and Accounting Precision Based on Simple Moving Average Zulham Sitorus; Lia Nazliana Nasution; Rahima Br Purba; Amnisuhaila Abarahan; Rowiyah Asengbaramae; Feby Wulandari Sembirinng; Mhd Ihsan Abidi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9505

Abstract

Fluctuations in demand for processed banana products often lead to inaccurate inventory planning at the MSME scale, resulting in decreased operational efficiency and potential accounting inaccuracies in inventory valuation and the calculation of Cost of Goods Sold (COGS). The calculation of raw material stock forecasting for 2024-2025 produces the following predicted values: 124 bunches of bananas, 80 pieces of chocolate, 81 kg of cooking oil, and 42 kg of granulated sugar. This simple, fast, and accurate forecasting process enables producers to more accurately predict product demand, ultimately reducing the risk of overstocking or shortages. This study aims to optimize sales strategies and inventory forecasting for processed banana products through a conceptual framework that integrates economic efficiency. The method used is the Simple Moving Average (SMA) to forecast inventory needs based on historical sales data at the BananaChips MSME, by testing several variations of the forecasting period to obtain the most stable and representative results. Overall, the recapitulation results show that the Cooking Oil raw material has the highest forecasting accuracy, with the lowest MAPE of 1.81% (MAD 1.50, MSE 5.20). Meanwhile, Granulated Sugar raw material recorded the highest MAPE value of 5.08% (MAD 2.25, MSE 9.73), followed by Chocolate (MAPE 2.43%) and Banana (MAPE 2.18%). The implementation results show an increase in stock management efficiency of up to 20% and a 15% decrease in excess raw materials. These findings indicate that integrating SMA forecasting with an economic efficiency framework and accounting accuracy can improve the quality of inventory and sales decision-making, thereby strengthening the profitability and sustainability of the banana-processed product business at the Bananachips MSME
SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY TOWARDS ELECTRIC MOTORCYCLES ON TWITTER USING ORANGE DATA MINING Zulham Sitorus; Maulian Saputra; Siti Nurhaliza Sofyan; Susilawati
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.9374

Abstract

This study explores sentiment analysis of the Indonesian community towards electric motorcycles on Twitter using Orange Data Mining. In the context of the increasing popularity of electric vehicles, especially electric motorcycles, understanding public sentiment becomes crucial for various stakeholders. Twitter, as a leading social media platform, serves as a rich source of opinions and discussions on various topics, including electric motorcycles. This research utilizes Orange Data Mining with multilingual sentiment analysis techniques to analyze the sentiment of the Indonesian community regarding electric motorcycles. The results of sentiment analysis are visualized through box plots and scatter plots, aiming to classify Twitter users based on their emotional responses. The findings of this study provide valuable insights into the sentiment landscape surrounding electric motorcycles in Indonesia, benefiting policymakers, manufacturers, and marketers in understanding public perception and making informed decisions.
Rancang Bangun Sistem Informasi Penjualan di The LDR Coffee Berbasis Web Menggunakan Native Programming Retno Mutiara; Zulham Sitorus; Andysah Putera Utama Siahaan
Jurnal Nasional Teknologi Komputer Vol 6 No 2 (2026): April 2026
Publisher : CV. Hawari

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Abstract

Perkembangan teknologi informasi mendorong kebutuhan akan sistem yang efisien dalam mengelola operasional bisnis, termasuk pada kafe. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Informasi Penjualan Berbasis Web di The LDR Coffee menggunakan pengembangan native programming. Sistem dirancang untuk mempermudah pencatatan transaksi, pengelolaan data minuman dan meja, serta penyusunan laporan penjualan secara real-time. Metode penelitian yang digunakan adalah Waterfall, dimulai dari analisis kebutuhan hingga implementasi, dan pengujian dilakukan menggunakan Black Box Testing untuk memastikan semua fitur berjalan sesuai kebutuhan pengguna. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan kecepatan, akurasi, dan efisiensi proses penjualan, serta meminimalkan kesalahan yang umum terjadi pada metode manual. Sistem ini juga memudahkan monitoring penjualan dan mendukung pengambilan keputusan manajerial secara lebih tepat.
Perancangan Platform Web Marketplace Personal Untuk Freelance Digital Artist Dengan Metode Research & Development (R&D) Hilal Prayogi; Zulham Sitorus; Zulfahmi Syahputra
Jurnal Nasional Teknologi Komputer Vol 6 No 2 (2026): April 2026
Publisher : CV. Hawari

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Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan platform marketplace berbasis web personal untuk freelance digital artist menggunakan metode Research and Development (R&D) dengan model ADDIE. Berdasarkan survei terhadap 17 digital artist, teridentifikasi pain points signifikan pada platform existing seperti biaya komisi tinggi (41,2%), proses verifikasi rumit (47,1%), dan persaingan tidak sehat dengan karya AI (Artificial Intelligence) (52,9%). Platform dikembangkan menggunakan Laravel 12, PHP, MySQL, HTML, CSS, dan JavaScript, dengan delapan fitur utama: Profile, Link, Portfolio, Commission Management, Review & Rating, Management Review, Real-time Chat, FAQ, dan Contact. Evaluasi yang melibatkan 15 responden menunjukkan bahwa platform mencapai tingkat kelayakan 85,6% dengan kategori "Sangat Layak". Aspek Usability memperoleh skor tertinggi yaitu 88,8%, sedangkan Functionality memperoleh skor 88,0%, yang menunjukkan bahwa fitur yang disediakan sesuai dengan kebutuhan digital artist Indonesia. Platform ini berhasil menjawab tantangan pemasaran yang dihadapi digital artist dengan menyediakan solusi terintegrasi dengan model komisi 0%, sehingga artist dapat memperoleh margin keuntungan maksimal.
Development of a distribution network electricity supply regulation system using machine learning at PT. PLN (Persero) UP2D North Sumatra Yasri, Afif; Zulham Sitorus
Bahasa Indonesia Vol 18 No 02 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i02.491

Abstract

The regulation of efficient energy supply within the distribution network is a significant difficulty for PT. PLN (Persero) UP2D North Sumatra in delivering dependable and stable services. This project seeks to create an automated system for regulating power supply in the distribution network through machine learning techniques. This system aims to forecast and enhance the allocation of electrical energy utilising historical data and current network circumstances. The dataset is partitioned into training data (training set) and testing data (testing/validation set) in specific ratios, such as 70%:30% or 80%:20%. The division is executed to preserve the temporal sequence (in time series scenarios) or to ensure a balanced representation (in classification scenarios). The employed machine learning approach is K-Means Clustering, utilised to analyse electricity consumption patterns and identify probable problems in the distribution network. The new centroid computation is based on the fact that each cluster contains a single data point, specifically C1 = (193, 205, 213), C2 = (153, 167, 170), and C3 = (179, 196, 200). This study's findings are anticipated to enhance the efficiency of energy distribution management, minimise downtime, and elevate the quality of service for consumers. By using a machine learning-driven automation system, PT. PLN UP2D North Sumatra can enhance its adaptability to load fluctuations and optimise the utilisation of current power supplies. The division is executed to preserve the temporal sequence (in the case of time series) or to ensure a balanced representation (in the case of classification).
Fault Detection And Recovery System On 20 Kv Distribution Network Using Real-Time Analysis With Support Vector Machine Algorithm Afrizal, Henri; Zulham Sitorus; Muhammad Syahputra Novelan
Bahasa Indonesia Vol 18 No 02 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i02.494

Abstract

The 20 kV distribution network is crucial for ensuring the uninterrupted supply of power to users in the PT. PLN UP2D North Sumatra Region. Disruptions in this network, including short circuits, overloads, and transient disturbances, can diminish system reliability and prolong outage durations if not promptly identified and rectified. This study seeks to develop a disturbance detection and recovery system for a 20 kV distribution network utilizing real-time analysis through the Support Vector Machine (SVM) algorithm. The system is designed by utilizing real-time electrical parameter data, including current, voltage, and network operational conditions, sourced from monitoring devices. The data undergoes preprocessing, feature extraction, and classification stages utilizing SVM to differentiate between normal and fault circumstances. The classification outcomes serve as the foundation for decision-making in isolating the fault zone and restoring supply to the unaffected segments of the network. The system's performance is assessed according to detection accuracy, response speed, and its capacity to facilitate the disturbance recovery process both automatically and semi-automatically. This research aims to enhance the dependability of the 20 kV distribution network, expedite fault resolution, and facilitate the advancement of a more intelligent, efficient, and responsive electrical distribution system.
Framework Data-Driven Customer Analytics pada Sistem Informasi Reservasi dan Transaksi untuk Peningkatan Kinerja Barbershop Muhammad Hafizh Al-Ghifari Rangkuti; Septia Harliansyah; Solly Aryza; Zulham Sitorus
JET (Journal of Electrical Technology) Vol 11, No 1 (2026): EDISI FEBRUARI
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i1.13799

Abstract

Industri barbershop menghadapi tantangan dalam meningkatkan kualitas layanan dan efisiensi operasional seiring meningkatnya kebutuhan pelanggan terhadap layanan yang cepat, personal, dan berbasis digital. Sebagian besar sistem reservasi dan transaksi yang digunakan saat ini masih berfokus pada pencatatan operasional tanpa memanfaatkan data pelanggan sebagai sumber informasi strategis untuk mendukung pengambilan keputusan. Penelitian ini bertujuan untuk mengembangkan Framework Data-Driven Customer Analytics yang terintegrasi dengan sistem informasi reservasi dan transaksi guna meningkatkan kinerja operasional dan kualitas layanan pada usaha barbershop. Framework yang diusulkan memanfaatkan data reservasi, transaksi, frekuensi kunjungan, preferensi layanan, dan pola perilaku pelanggan untuk menghasilkan informasi analitik yang mendukung pengambilan keputusan berbasis data. Pengembangan sistem dilakukan menggunakan metode Waterfall yang meliputi analisis kebutuhan, perancangan framework, implementasi sistem, dan pengujian. Evaluasi sistem dilakukan melalui pengujian fungsional menggunakan Black Box Testing serta analisis kinerja berdasarkan indikator operasional dan layanan pelanggan. Hasil penelitian menunjukkan bahwa framework yang dikembangkan mampu mengintegrasikan data pelanggan secara efektif, menyediakan informasi analitik yang relevan bagi pengelola, meningkatkan efisiensi proses reservasi dan transaksi, serta mendukung penyusunan strategi layanan yang lebih tepat sasaran. Kontribusi penelitian ini terletak pada pengembangan model customer analytics berbasis data yang dapat dimanfaatkan sebagai dasar pengambilan keputusan untuk meningkatkan daya saing dan keberlanjutan bisnis barbershop di era transformasi digital. Kata Kunci: Customer Analytics, Data-Driven Framework, Sistem Informasi Reservasi, Sistem Transaksi, Barbershop, dan Pengambilan Keputusan Berbasis Data.
Application of the C45 Algorithm to Predict Student Academic Scores Andi Ernawati; Zulham Sitorus; Ananda Aulia; Ayu Ofta
Bulletin of Information Technology (BIT) Vol 5 No 2 (2024): Juni 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i2.1251

Abstract

Student grades are the results of teaching and learning activities on a campus. So you can know your target for completing your studies. This research uses the C4.5 Algorithm which can help predict the results of student assessments. The dataset consists of student achievement index, place of residence, discipline, lecturer's role in lectures. From 40 datasets we have obtained a decision on student academic achievement and obtained performance results from accuracy results of 86.36% with class precision predicate Yes=84.62%, No=88.89% and class recall Yes=91.67%, No=80.00%.
Perancangan Antarmuka Website Labusel Creative sebagai Media Informasi Daerah Menggunakan Metode UCD Rangga Rafandi; Zulham Sitorus; Barany Fachri
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.828

Abstract

Bobabox, sebuah usaha minuman di Medan, menghadapi tantangan operasional akibat proses transaksi dan manajemen inventaris yang masih dilakukan secara manual. Proses ini rentan terhadap kesalahan pencatatan, ketidakakuratan data stok, dan menghambat analisis data penjualan untuk pengambilan keputusan strategis. Penelitian ini bertujuan untuk merancang dan membangun sebuah aplikasi Point of Sale (POS) berbasis website sebagai solusi. Metode perancangan yang digunakan adalah model Waterfall, yang meliputi tahapan analisis, perancangan dengan pemodelan UML, implementasi, dan pengujian. Aplikasi dikembangkan menggunakan framework Laravel dan diuji menggunakan metode Black Box Testing. Hasil dari penelitian ini adalah sebuah sistem Web-POS yang fungsional, mencakup modul manajemen data, modul transaksi interaktif, dan modul pelaporan dinamis. Berdasarkan pengujian, seluruh fungsionalitas sistem dinyatakan Valid. Kesimpulannya, aplikasi Web-POS yang dibangun mampu menjadi solusi efektif untuk meningkatkan efisiensi operasional, akurasi data, dan mendukung pengambilan keputusan berbasis data guna mengoptimalkan penjualan di Bobabox
Co-Authors , Arpan , Fery Anugerah , Rahima Br Purba A.A. Ketut Agung Cahyawan W Abda Abda Ade Surya Bakti Pane Afrizal, Henri Afrizal, Sandi Aldi Kesuma Alvian Alvian Ami Abdul Jabar Amnisuhaila Abarahan Ananda Aulia Andi Ernawati Andi Ernawati Andysah Putera Utama Siahaan Angkat, Chairul Indra Anshari, Ari Antoni, Robin Ardya, Dwika Arief, Muhammad Arif Rahman Astri Mutia Rahma Audry, Beby Aulia, Ananda Ayu Ofta Azhari, M. Idrus azwan, m Baehaqi Bambang Sugito Batubara, Supina Boy Rizki Akbar Br Tarigan, Sella Monika Chelfina Utami Daniel Happy Putra Danu Wardhana Azhari Darmeli Nasution DEWI SARTIKA diansyah, Suhar Diva, Krisna Eko Hariyanto Eko Hariyanto Eko Hariyanto Eko Wahyudi Erbin Sitorus Fachri, Barany Fahmi Izhari Fahmi Kurniawan Farta wijaya, Rian Feby Wulandari Sembirinng Fikri Zuhaili Simbolon Gilang Ramadhan Gultom, Ananda Christianto Hafiz Rodhiy Haliza, Siti Nur Hamzah, Iswadi Harmiati Bungsu Bangun Hartono Sinambela, Sugi Helmy, Ahmad Hendra Harnanda Heni Wulandari Hilal Prayogi Hrp, Abdul Chaidir Ibezato Zalukhu, Anzas Ika Devi Perwitasari Indra Angkat, Chairul IQBAL , MUHAMMAD Irwan Syahputra Irwan Syahputra, Irwan Khairul Khairul Khairul, Khairul Kiki Artika Kurniawan, Fahmi Laila Maghfirah Larius Ambasador Parlindungan Leni Marlina Leni Marlina Lia Nazliana Nasution Limbong, Yohannes France M Imam Santoso M. Rasyid M.Rizki Khadafi Mardiah, Nia Marzuki Sianturi, Ismail Maulian Saputra Melva Sari Panjaitan Meri Sri Wahyuni Mhd Arie Akbar Mhd Ihsan Abidi Mohammad Yusuf, Mohammad Muhammad Fahriza Muhammad Hafizh Al-Ghifari Rangkuti Muhammad Iqbal Muhammad Irfan Sarif Muhammad Syahputra Novelan Muhammad Wahyudi Nahampun, Natalia Nainggolan, Andreas Ghanneson Nainggolan, Irfan Nazar Saputra, Risfan Nelviony Parhusip Nurwijayanti Ofta Sari, Ayu Parhusip, Nelviony Pasaribu, Ryan Fahreza Pranoto, Sugeng Putra, Khairil Ragil Satya Adi W Rahmat Hidayat Ramadani, Pebri Ramadhan, Aditya Ramadhani, Aditya Rangga Rafandi Razaq, Abdul Retno Mutiara Rian Farta Wijaya Rian Putra, Randi Rika Uli Samosir, Siska Risky, Raihan Rowiyah Asengbaramae Rusydi Tanjung , Miftah Sahputra, Fajar Said Oktaviandi Septia Harliansyah Septia Harliansyah Septiani, Nadya Sianturi, Ismail Sibarani, Dina Marsauli Simamora, Siska Simorangkir, Elsya Sabrina Asmita Sinambela, Sugi Hartono Sinyo Andika Nasution, Ahmad Sipra Barutu Siregar, Andree Risky Yuliansyah Sitepu, Fernando Siti Nurhaliza Sofyan Sitinur, Siti Nurhaliza Sofyan Sitompul, Jelly Rolley Sofyan, Siti Nurhaliza Solly Ariza Lubis Solly Aryza Sri Wahyuni, Meri Suhardiansyah Suhardiansyah Suhardiansyah Suherman Suherman Sukrianto, Sukrianto Sutiono, Sulis Syahputri, Maulisa Syamsiar, Syamsiar T, Siti Isna Syahri Tanjung, Miftah Rusydi Tiara Aninditha Tumangger, Oktavia Utama, Hendra Vina Arnita Vivin Yulfia Sarah Wahyu Agung Pratama Wahyuni, Meri Sri Wijaya, Rian Farta Wirda Fitriani Yahya, Susilawati Yasri, Afif Zai, Yulianus Zalukhu, Anzas Ibezato Zulfahmi Syahputera Zulfahmi Syahputra Zulfahmi Zulfahmi Zulfahmi Zulfahmi