Article Per Year (5 Year)
p-Index From 2021 - 2026
5.376
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika 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 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)
Muhammad Fikry
Teknik Informatika Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 50 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 50 Documents
Search

 1   2   3   4   5 
Development of an IoT-Based Smart Greenhouse with Fuzzy Logic for Chrysanthemum Cultivation Khairina, Jikti; Nurdin, Nurdin; Fikry , Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10313

Abstract

Conventional cultivation of Chrysanthemum plants in greenhouses faces serious challenges such as inefficiency, response delays, and errors in temperature and humidity settings due to manual management. These conditions result in unsuitable growing environments that can reduce the quality and quantity of harvests. To overcome these problems, this study developed a smart greenhouse system based on the Internet of Things (IoT) and cloud computing with the application of fuzzy logic. The system is designed to automatically monitor and control temperature, humidity, and light intensity using NodeMCU ESP32, DHT22 and BH1750 sensors, as well as relay-based actuators and mini air conditioners. Environmental data is sent to the cloud and processed using the Sugeno fuzzy method to produce adaptive and precise control decisions. Test results show that the system can maintain stable and optimal environmental conditions with an average temperature control difference of 30.341% and an actuator efficiency of 9.34% against microcontroller commands. This system provides a modern solution to the limitations of traditional methods, and supports smart agriculture in tropical climates such as Lhokseumawe.
SYSTEM FOR MONITORING CONSUMABLE WATER QUALITY BASED ON INTERNET OF THINGS Muhammad Sapriadi; Muhammad Fikry; Ar Razi
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 6 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i6.3472

Abstract

Access to clean and consumable water is a critical factor for public health, but not all communities have the tools to monitor water quality in real-time. To address this issue, this study proposes the development and implementation of an Internet of Things (IoT)-based water quality monitoring system. The system is designed to measure key water quality parameters such as pH, Total Dissolved Solids (TDS), turbidity, and temperature, using an ESP32 microcontroller. This microcontroller is connected to a variety of sensors and integrated with the Blynk application, which allows users to monitor the data through their mobile devices. The system also incorporates a rule-based decision-making method that classifies the water as consumable or not, based on predetermined standards. The IoT-based system ensures smooth data transmission to the Blynk app, and the decision-making process is accurate and reliable. This system provides a practical and efficient solution for real-time water quality monitoring, especially for communities lacking advanced water quality monitoring tools. It enables users to assess water quality remotely, offering a significant improvement in public health monitoring and management.
Pengembangan Sistem Pakar Diagnosa Hipertensi Berbasis Web Menggunakan Klasifikasi Naive Bayes Rauzana, Rauzana -; Asrianda, Asrianda -; Muhammad Fikry, Muhammad Fikry -; Ramadhani, Ramadhani -
Journal of Tourism Sciences, Technology and Industry Vol 4, No 2 (2025): JTSTI-Journal of Tourism Science, Technology and Industry
Publisher : Institut Seni Indonesia Padangpanjang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26887/jtsti.v4i2.6208

Abstract

Hypertension is a degenerative disease often referred to as a silent killer because it can trigger serious complications such as stroke, heart failure, and kidney damage. Limited access to medical personnel causes delays in diagnosis and treatment. This study designs a web-based expert system to diagnose hypertension using the Naive Bayes method. The system was developed using PHP programming language and MySQL database. Testing was conducted on 6 datasets, resulting in a diagnostic accuracy of 80%. This system is expected to help the community recognize hypertension symptoms and provide preventive and initial treatment solutions.
Implementasi Metode RBMT dalam Penerjemahan Bahasa Indonesia ke Bahasa Makassar Hanif, Wan Muhammad; Yusra, Yusra; Muhammad Fikry; Febi Yanto; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.935

Abstract

?This research was conducted to address the limited availability of linguistic resources for regional languages, particularly Makassar Language, which does not yet have adequate automatic translation support. The main problem addressed in this study is the absence of a reliable automatic translation system for Makassar Language. The objective of this research is to apply a rule-based translation method to translate text from Indonesian into Makassar Language. This study focuses on the implementation of the Rule-Based Machine Translation (RBMT) method for translating Indonesian text into Makassar Language using the Python programming language. The RBMT implementation involves tokenization, morphological analysis, vocabulary matching, and the application of grammatical rules, including the identification of prefixes and suffixes. The data used consist of a bilingual dictionary compiled from various sources and a set of test sentences representing everyday sentence structures. Translation evaluation was carried out using the Word Error Rate (WER) method, yielding a result of 0.289, and the Character Error Rate (CER) method, with a result of 0.21, which fall into the “Good” category based on the evaluation scale. The main findings indicate that the application of the RBMT method is capable of producing reasonably accurate translations at both the word and character levels. These findings demonstrate that a rule-based approach can be effectively applied to regional languages with limited digital data and provide an initial overview of the potential use of rule-based methods to support the development and preservation of regional languages.
IMPLEMENTASI MACHINE LEARNING UNTUK PREDIKSI PENGELUARAN KEUANGAN BERDASARKAN POLA EKSTERNAL DAN INTERNAL (SEASONALITY, KEGIATAN RUTIN & INSIDENTIL) STUDI KASUS: FAKULTAS TEKNIK UNIVERSITAS ALMUSLIM Hajar, Siti; Asrianda, Asrianda; Fikry, Muhammad
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.6008

Abstract

Perencanaan anggaran yang akurat merupakan faktor penting dalam pengelolaan keuangan perguruan tinggi. Fakultas Teknik Universitas Almuslim menghadapi fluktuasi pengeluaran yang dipengaruhi oleh pola internal dan eksternal, seperti seasonality, kegiatan rutin akademik, serta kegiatan insidentil. Penelitian ini bertujuan untuk mengimplementasikan metode machine learning dalam memprediksi pengeluaran keuangan fakultas berdasarkan pola-pola tersebut. Data historis pengeluaran keuangan pada anggaran tahun 2021 – 2025 digunakan sebagai dataset, yang dikombinasikan dengan variabel waktu dan jenis kegiatan. Tahapan penelitian meliputi preprocessing data, pemodelan, serta evaluasi kinerja model menggunakan metrik kesalahan prediksi. Hasil penelitian menunjukkan bahwa model machine learning mampu menghasilkan prediksi pengeluaran yang lebih akurat dibandingkan metode perencanaan konvensional. Model prediksi ini diharapkan dapat menjadi alat bantu pengambilan keputusan dalam penyusunan anggaran, meningkatkan efisiensi pengelolaan keuangan, serta mendukung penerapan data-driven decision making di lingkungan Fakultas Teknik.
The Correlation Of Factors Causing Divorce In Households Using The Apriori Data Mining Algorithm Amalia, Iklasni; Fikry, Muhammad; Asrianda, Asrianda; Khaidar, Al
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8518

Abstract

Mahkamah Syari’ah merupakan lembaga di bawah Mahkamah Agung yang mempunyai misi melayani masyarakat dalam urusan rumah tangga dan kesejahteraan hukum, termasuk perkara perceraian. Aceh Tengah merupakan salah satu kabupaten dengan tingkat percerain yang sangat tinggi yang ada di Aceh dan terus menerus meningkat setiap tahun nya . Tujuan dari penelitian ini guna untuk salah satu cara dalam mencegah terjadinya perceraian yang ada di kabupaten Aceh Tengah, dengan melihat faktor-faktor yang menyebabkan terjadinya perceraian di Aceh Tengah serta korelasi antar faktor terserbut, faktor-faktor yang dicari dibentuk dengan sebuah hubungan yang di sebut Association Rules. Association Rules meruapakan  salah satu metode yang bertujuan untuk mencari pola yang yang sering muncul diantara banyak nya faktor dari beberapa item.  Association Rules ini akan digunakan dalam algoritma Apriori sehingga dapat digunakan untuk mencari korelasi faktor-faktor penyebab perceraian di Aceh Tengah. penelitian ini menggunakan data perceraian yang ada Mahakamah Syari’ah Aceh Tengah pada tahun 2021.Penelitian ini diharapkan akan menghasilkan temuan yang bermanfaat dalam memberikan kontribusi positif bagi masyarakat dalam mencegah terjadinya perceraian yang ada di Aceh Tengah, selain itu diharapkan dapat membuka wawasan baru mengenai pemanfaatan teknik pembelajaran mesin dalam bidang permasalahan perkara-perkara gugatan
Implementation Of Single Moving Average In Gold Price Movement Analysis Muqarrabin, Khalis Al; Fikry, Muhammad; Asrianda, Asrianda; Khaidar, Al
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8519

Abstract

Penelitian ini didasarkan pada pentingnya prediksi harga emas sebagai salah satu komoditas yang memiliki volatilitas tinggi. Permasalahan yang diangkat adalah ketidakpastian pergerakan harga emas yang memerlukan metode peramalan yang sederhana namun efektif. Tujuan dari penelitian ini untuk memberikan wawasan lebih dalam tentang bagaimana SMA dapat digunakan dalam analisis pergerakan harga emas dan membantu investor dalam membuat stategi investasi yang lebih baik. Penelitian ini menggunakan metode Single Moving Average (SMA) untuk menganalisis pergerakan harga emas, dengan SMA dihitung berdasarkan rata-rata harga penutupan emas selama 5 dan 10 hari. Akurasi prediksi dievaluasi menggunakan Mean Absolute Error (MAE) dan Mean Squared Error (MSE) yang membandingkan hasil perhitungan SMA dengan harga emas aktual untuk menilai efektivitas metode ini. Hasil penelitian menunjukkan bahwa metode SMA cukup akurat dalam meramalkan tren harga emas jangka pendek, meskipun terdapat sedikit keterlambatan dalam respons terhadap perubahan harga yang mendadak. Metode SMA dapat menjadi alat peramalan yang sederhana dan efektif untuk tren harga emas, terutama untuk periode jangka pendek.
Public Sentiment Analysis on the November 2025 Flood Disaster in Aceh Using Natural Language Processing and Lexicon-Based Approach Erwanda, Ade Putra; Khaidar, Al; Asrianda, Asrianda; Fikry, Muhammad; Khaldun, Ibnu
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8481

Abstract

Bencana banjir yang melanda Provinsi Aceh pada November 2025 merupakan salah satu bencana hidrometeorologi besar yang berdampak luas terhadap kehidupan masyarakat. Banjir terjadi di 16 kabupaten/kota dan mengakibatkan hampir 120 ribu jiwa terdampak, puluhan ribu warga mengungsi, serta kerusakan signifikan pada permukiman dan infrastruktur. Peristiwa ini memicu respons publik yang masif di media sosial, khususnya Instagram. Penelitian ini bertujuan untuk menganalisis sentimen respons masyarakat terhadap bencana tersebut menggunakan pendekatan Natural Language Processing (NLP) berbasis lexicon. Data diperoleh melalui proses data crawling terhadap 2.790 komentar Instagram, yang selanjutnya diproses melalui tahapan text cleaning, case folding, tokenization, stopword removal, dan stemming. Hasil analisis menunjukkan dominasi sentimen positif sebesar 62,51%, diikuti sentimen netral 24,98% dan negatif 12,51%. Temuan ini menunjukkan adanya apresiasi, harapan, serta kritik masyarakat terhadap penanganan bencana, dan dapat menjadi bahan evaluasi bagi pemangku kebijakan dalam meningkatkan strategi penanganan dan komunikasi bencana berbasis data.
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 (On Progress)
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%.
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.
Co-Authors Aldo januansyah. H Amalia, Iklasni Annisa Helmina Aprian Gigin Prasetia Ar Razi Asrianda Asrianda Asrianda, Asrianda - Aynun, Nur Azzahra Iskandar, Farah Bustami Bustami Bustami Chrisnata Manihuruk Cut Ita Erliana Dahlan Abdullah David Fadlianda Dessayani Putri Dyah Ika Rinawati Ella Suzanna Erwanda, Ade Putra Eva Darnila Fadlisyah Fadlisyah Faiz Syukri Arta Faiz Fajar Rivaldi Chan Fajriana, Fajriana Faradilla, Cut Meutia Febi Yanto Gusti, Siska Kurnia Hafizh Al Kautsar Aidilof Hanif, Wan Muhammad Hasan Tahir Helmi Naluri Herman Fithra Hidayatsyah Hidayatsyah Hizamrul jaen Ibnu Khaldun Ima Pratiwi Imam Rosadi Irfan Sahputra Iskandar, Fahra Azzahra Ismail Ismail Khaidar, Al Khairina, Jikti Kurnia Amanda, Destiara Kurniawati Kurniawati Lidya Rosnita Luthvy Ilhamdi M Ishlah Buana Angkasa Maharani, Silfa Mhd Firza Ryzaaldy Muchlis Abdul Muthalib Muhammad Al Imran Muhammad Dastur Muhammad Iqbal Muhammad Iqbal Muhammad Sapriadi Muhammad Yani, Muhammad Mukhlis Mukhlis Muqarrabin, Khalis Al Nazwa Aulia NELI SUSANTI, NELI Nunsina Nura Usrina Nurdin Nurdin Rahma, Mutiara Ramadhani, Ramadhani - Rauzana, Rauzana - Rifkial Iqwal Rini meiyanti Risawandi, Risawandi Rizal S.Si., M.IT, Rizal Rizki Suwanda Rozzi Kesuma Dinata Safwandi Safwandi Said Fadlan Anshari Sari, Cut Jora Sembiring, Vivi Dista Br Silfa Maharani Br Padang Siti Hajar Subhan Hartanto Subhan Hartanto Sudirman Sudirman Sujacka Retno Sukma Rizki Taufiq Taufiq Taufiqurrahman Taufiqurrahman Tejas Shinde Umar Khalil Wahdana, Aldi Yani, Muhamamd Yesy Afrillia Yesy Afrillia Yusra, Yusra Zahratul Fitri Zara Yunizar zulfhazli zulfhazli