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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) METIK JURNAL Vocatech : Vocational Education and Technology Journal Jurnal Sosial Humaniora Sigli Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Bulletin of Computer Science Research JUTECH : Journal Education and Technology Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) TECHSI - Jurnal Teknik Informatika Journal of Information Technology (JINTECH) Jurnal Teknologi Terapan and Sains 4.0 Journal of Computer Engineering, Electronics and Information Technology Journal Of Artificial Intelligence And Software Engineering Jurnal Informatika: Jurnal Pengembangan IT Journal of Tourism Sciences, Technology and Industry Jurnal Malikussaleh Mengabdi Teewan Journal Solutions Proceeding of The International Conference on Electrical Engineering and Informatics Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Muhammad Fikry
Teknik Informatika Universitas Malikussaleh
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Implementation of Double Exponential Smoothing to Forecast the Number of Outpatient Visits at Arun Hospital Sembiring, Vivi Dista Br; Fikry, Muhammad; Asrianda, Asrianda
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8497

Abstract

Seiring peningkatan kesadaran masyarakat mengenai kesehatan bisa meningkatkan angka kunjungan di rumah sakit. Pasien yang berkunjung sangat bervariasi serta tidak bisa diprediksi tentu mengakibatkan rencana yang dibangun tidak efektif. Hal ini harus diantisipasi dengan memperkirakan atau memprediksi jumlah pasien yang berkunjung. Oleh karena itu, dalam penelitian ini dibangun sistem perkiraan jumlah kunjungan pasien rawat jalan dengan metode Double Exponential Smoothing. Penelitian ini dilakukan pada Rumah Sakit Arun serta data yang diambil dari 11 poliklinik yang ada pada rumah sakit dari Januari tahun 2020 hingga Desember 2023. Hasil dari penelitian ini ialah perkiraan pada poliklinik hemodialisis sebanyak 9 orang, poliklinik bedah 34 orang, poliklinik gigi dan mulut 6 orang, poliklinik jiwa 24 orang, poliklinik kesehatan anak 28 orang, poliklinik mata 24 orang, poliklinik obgyn ibu hamil 6 orang, poliklinik orthopedi 13 orang, poliklinik paru 34 orang, poliklinik penyakit dalam 39 orang, dan terakhir poliklinik syaraf 46 orang. Dengan hasil perhitungan rata-rata persentase error pada poliklinik hemodialisis selama setahun yaitu 0,90%.
Development of an Information Website as a Publication Medium for the Malikussaleh Airport Organizing Unit in Lhokseumawe Raihansyah, Khananda; Asrianda; Muhammad Fikry
Teewan Journal Solutions Vol. 2 No. 4 (2025): Desember
Publisher : Teewan Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/c8sdmf21

Abstract

Malikussaleh Airport currently uses social media such as Instagram to share information, which results in limited information accessibility for visitors regarding news, profiles, and airport activities. This study aimed to develop a website-based information system to facilitate employees in disseminating information and ease public access to updated airport news. The system was built using PHP programming language, MySQL database, and designed through Data Flow Diagrams (DFD). The implementation produced an informative and accessible platform that allows the airport management to provide transparent news, flight updates, and activity galleries. The results showed that the website effectively improved the information distribution process compared to previous social media-only methods.
Comparative Performance Analysis of Traditional (SFQ, PCQ) and Modern (FQ-CoDel, CAKE) Queuing Algorithms on MikroTik RouterOS v7 for Broadband Network QoS Optimization Maulana, OK Muhammad Majid; Asrianda; Muhammad Fikry
Teewan Journal Solutions Vol. 2 No. 4 (2025): Desember
Publisher : Teewan Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/8vbsxe71

Abstract

Bufferbloat in broadband networks often leads to high latency, which degrades the Quality of Experience (QoE), particularly for time-sensitive activities. MikroTik RouterOS v7 introduces modern Active Queue Management (AQM) algorithms, such as FQ-CoDel and CAKE, which are claimed to outperform traditional algorithms like SFQ and PCQ. This study aims to analyze and compare the performance of these four queuing algorithms in a single-link gateway scenario on a 35 Mbps internet service. The research methodology employs a quantitative experimental approach by saturating the network with heavy traffic. Quality of Service (QoS) parameters including throughput, delay, jitter, and packet loss—were measured based on TIPHON standards. The results indicate that modern algorithms maintain latency stability under full load significantly better than traditional ones. This study recommends the use of FQ-CoDel for resource efficiency and CAKE for maximum quality
Exploratory Data Analysis and Machine Learning Approaches for Early Detection of Student Depression Muhammad Fikry; Bustami Bustami; Ella Suzanna
Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/iceei.v1i2.31

Abstract

This study conducts an exploratory data analysis combined with machine learning techniques to identify early signs of student depression. We investigated various factors affecting mental health among students, including sleep duration, dietary patterns, history of suicidal thoughts, family history of mental illness, and their relationships with depression across age groups and academic pressure. The study also examined the influence of gender on academic stress levels. Three machine learning models such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were utilized to predict depression. The performance of these models was evaluated, achieving accuracy rates of 84.97% for Random Forest, 84.85% for SVM, and 81.16% for KNN. The findings highlight the effectiveness of these models in predicting student depression and underscore the importance of targeted mental health interventions based on key factors influencing mental health among students.
Utilizing Sarima For Seasonal Forecasting Of Coffee Production In Aceh Province, Indonesia Aprian Gigin Prasetia; Muhammad Fikry; Yesy Afrillia
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

In this paper, we forecast coffee production in Aceh Province, Indonesia, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Coffee is a critical commodity for the region’s economy, contributing significantly to both local income and export revenues. Accurate forecasting of coffee production is essential for economic planning, supply chain management, and strategic development in the coffee sector. Using secondary data from the Indonesian Central Bureau of Statistics, we identified the SARIMA (2,0,1)(1,1,1)12 model as the best fit, with a Mean Absolute Percentage Error (MAPE) of 12.94%. The forecasting results for the period of 2023 to 2024 reveal a consistent seasonal pattern in line with historical data, though a slight decline in production is projected. Notably, the lowest production of 22 tons occurred in February 2019, while the highest, 21,408 tons, was recorded in July 2019. These findings provide valuable insights for policymakers and stakeholders in the coffee industry, offering a robust basis for developing targeted interventions to enhance production and manage fluctuations. The results underscore the importance of reliable forecasting models like SARIMA in supporting sustainable growth and decision-making in regional agricultural sectors.
Performance Evaluation of ARIMA Model in Forecasting Rice Production Across Sumatera, Indonesia Imam Rosadi; Muhammad Fikry; Hafizh Al kautsar Aidilof
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Abstract In this paper, we present a comprehensive performance evaluation of the ARIMA (AutoRegressive Integrated Moving Average) model in forecasting rice production across Sumatera, Indonesia. Rice is a crucial staple crop, feeding more than half of the global population. In Sumatera, rice plays a vital role in food security, yet its cultivation is highly dependent on specific environmental conditions such as temperature, humidity, and rainfall. This study leverages historical time-series data from the years 2000 to 2020, collected from eight key provinces: Aceh, North Sumatera, West Sumatera, South Sumatera, Riau, Jambi, Bengkulu, and Lampung. The objective is to forecast rice production for the years 2021-2024 using the ARIMA method. Through rigorous model selection and evaluation, ARIMA (3,0,2) was identified as the most suitable model, providing accurate forecasts with a Mean Squared Error (MSE) of 0.0325 and a Mean Absolute Error (MAE) of 0.1445. These low error rates demonstrate the model’s capacity to capture the inherent fluctuations in rice production trends across Sumatera. The findings offer critical insights for future rice production trends and can guide policy-makers in formulating effective food security strategies. This research contributes significantly to the understanding of rice production dynamics and the application of ARIMA models in agricultural forecasting. Keywords: Rice production; Mean Squared Error; Mean Absolute Error; ARIMA; Sumatera.
Predictive Analysis of Retail Promotion Strategies in the Context of Consumer Shopping Behavior Ima Pratiwi; Muhammad Fikry; Sujacka Retno
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

In this paper, we examine the impact of various promotional strategies on consumer shopping interest, focusing on the Alfamart retail chain in Lhokseumawe City, Indonesia, which saw rapid expansion from five to fifteen stores between 2017 and 2023. Despite this growth, expected sales increases have not been met, raising concerns about the effectiveness of current promotional tactics. Utilizing multiple linear regression analysis, we investigate the influence of three specific strategies, Promo Spesial Mingguan, Serba Gratis, and Tebus Murah on shopping interest across the 15 stores. Findings reveal that Tebus Murah is the most effective strategy in boosting shopping interest, showing the smallest error margin between predictive and actual sales figures. This study provides comprehensive insights into the broader effects of promotional strategies on consumer interest, highlighting the need for Alfamart to focus on optimizing the Discounted Redemption approach to maximize sales. The predictive system developed serves as a strategic tool for identifying effective promotions, forecasting sales, calculating return on investment, and analyzing consumer behavior. Our results underscore the value of predictive analysis in refining promotional strategies, enabling Alfamart to adopt a more targeted and efficient marketing approach to enhance sales performance.
Smart Fire Prevention: An IoT Approaceh To Detecting LPG Leaks And Fire Hazards Luthvy Ilhamdi; Muhammad Fikry; Hafizh Al Kautsar Aidilof
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

In this paper, we address the serious fire risks posed by Liquefied Petroleum Gas (LPG) leaks, which can lead to significant material damage and loss of life. These incidents are often caused by human error or the absence of effective early warning systems capable of timely leak detection. To tackle this issue, we have developed an automatic gas leak detection system integrated with the Internet of Things (IoT). The system utilizes the ESP32 microcontroller as the main control unit, along with an MQ2 gas sensor for detecting LPG leaks and a fire sensor for identifying fire hazards. Additional components include a fan to enhance air circulation in case of gas accumulation and an automatic water pump that activates upon fire detection, aiding in prompt fire extinguishment. The system is also equipped with an LCD to display real-time gas levels in the environment, providing visual feedback to users. For enhanced functionality, this system connects to the Blynk application, allowing remote monitoring and control via smartphone. This feature enables users to receive instant notifications upon detecting gas leaks or fires and to manually control the fan or water pump if necessary. The primary objective of this system is to provide early detection and automatic response to gas leaks and fire hazards, thereby reducing the risk of fire-related accidents. This IoT-based approach offers a reliable solution to enhance safety by ensuring rapid responses to gas leaks and fires, ultimately minimizing damage and protecting lives.
Development of Portable IoT-Based Fish Pond to Enhance Freshwater Aquaculture Efficiency Rifkial Iqwal; M Ishlah Buana Angkasa; Nazwa Aulia; Subhan Hartanto; Tejas Shinde; Muhammad Fikry; Zara Yunizar
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This paper presents the development of iPooL, a portable Internet of Things (IoT)-based fish pond system designed to optimize freshwater fish farming, particularly in resource-constrained and urban environments. By integrating real-time monitoring of essential water parameters—such as pH, temperature, dissolved oxygen, and ammonia levels—iPooL ensures that optimal environmental conditions are maintained for fish health and growth. The system employs IoT sensors connected to an ESP32 microcontroller, which processes and transmits data to a cloud platform, enabling farmers to receive real-time alerts and manage their ponds via a mobile app. Field trials demonstrated that the iPooL system reduces fish mortality by 20% and improves fish growth rates by maintaining stable water conditions. Additionally, the automation of feeding schedules and water management reduces operational costs, particularly in labor and feed, resulting in a 30% increase in profitability. With an estimated return on investment (ROI) within one year, iPooL offers a cost-effective solution for both small- and medium-scale fish farmers. The system also promotes environmental sustainability by optimizing water usage and reducing the need for chemical additives. Its portability allows fish farming in non-traditional environments, such as urban rooftops, contributing to decentralized food production and reducing the environmental impact of transporting fish to urban markets. iPooL’s scalability, combined with future integration of artificial intelligence and renewable energy sources, positions it as a transformative tool for the aquaculture industry, supporting both economic development and sustainable farming practices.
Home Assistant With IoT Smart Solution For Smart Home Sukma Rizki; Muhammad Fikry; M Ishlah Buana Angkasa; Fajar Rivaldi Chan
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The advent of Internet of Things (IoT) technology has revolutionized various aspects of everyday life, particularly within the home environment. IoT-powered home assistants represent one of the primary implementations, offering intelligent automation and control solutions that enhance the modern home experience. This paper explores the implementation of IoT-based home assistants to improve convenience, security, and energy efficiency in smart homes. In addition, the challenges and future directions for the development of this technology are examined, with a focus on key areas such as device interoperability, data privacy and security, and user experience optimization. As demand for smart home solutions continues to rise, the integration of cloud computing, artificial intelligence (AI), and advanced communication protocols will further drive innovation in this field.
Co-Authors Aldo januansyah. H Amalia, Iklasni Ananda, Silvia Angela, Angela Annisa Annisa Annisa Helmina Aprian Gigin Prasetia Ar Razi Asrianda Asrianda Asrianda, Asrianda - Aynun, Nur Azzahra Iskandar, Farah Budi Bahreisy Bustami Bustami Bustami Chrisnata Manihuruk Cut Ita Erliana Dahlan Abdullah David Fadlianda Dessayani Putri Dimas Pratama, Dimas Dyah Ika Rinawati Ella Suzanna Erwanda, Ade Putra Eva Darnila Fadlisyah Fadlisyah Fadlisyah Fadlisyah Faiz Syukri Arta Faiz Fajar Rivaldi Chan Fajriana, Fajriana Faradilla, Cut Meutia Febi Yanto Gusti, Siska Kurnia Hadi Iskandar Hafizh Al Kautsar Aidilof Hamdhana, Defry Hanif, Wan Muhammad Hasan Tahir Helmi Naluri Herman Fithra Hidayatsyah Hidayatsyah Hizamrul jaen Hutagalung, Yorio Arwandi Wisdom Ibnu Khaldun Ida Wahyuni Ima Pratiwi Imam Rosadi Irfan Sahputra Iskandar, Fahra Azzahra Ismail Ismail Khaidar, Al Khairina, Jikti Kurnia Amanda, Destiara Kurniawati Kurniawati Lidya Rosnita Lutfi, Raihansyah Luthvy Ilhamdi M Ishlah Buana Angkasa M. Rafli Al Thoriq Mustafa Maharani, Silfa Mardiansyah, M Rizki Maulana, OK Muhammad Majid Mhd Firza Ryzaaldy Muchlis Abdul Muthalib Muhammad Al Imran Muhammad Dastur Muhammad Iqbal Muhammad Iqbal Muhammad Iqbal Maulana Muhammad Sapriadi Muhammad Yani, Muhammad Mukhlis Mukhlis Mukti Qamal Muqarrabin, Khalis Al Nazwa Aulia NELI SUSANTI, NELI Nunsina Nura Usrina Nurdin Nurdin nuryana nuryana, nuryana Rahma, Mutiara Raihansyah, Khananda Ramadhani, Ramadhani - Rauzana, Rauzana - Rifkial Iqwal Rini meiyanti Risawandi, Risawandi Rizal S.Si., M.IT, Rizal Rizki Suwanda Romi Asmara Rozzi Kesuma Dinata Safwandi Safwandi Said Fadlan Anshari Salahuddin Salahuddin Saputra, Ferdy Sari, Cut Jora Sayed Fachrurrazi Sembiring, Vivi Dista Br Silfa Maharani Br Padang Siti Hajar Siti Ramadhani Subhan Hartanto Subhan Hartanto Sudirman Sudirman Sujacka Retno Sukma Rizki Surya Agustian Tarigan, Anggun Kinanti Taufiq Taufiq Taufiqurrahman Taufiqurrahman Tejas Shinde Umar Khalil Utomo, Muhammad Fikri Wahdana, Aldi Yani, Muhamamd Yesy Afrillia Yesy Afrillia Yulinazira, Ulfa Yusra Yusra, Yusra Yusriyana, Yusriyana Yusrizal Hasbi Zahratul Fitri Zara Yunizar zulfhazli zulfhazli