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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) The Indonesian Journal of Computer Science
Achmad Ridwan
Universitas Prima Indonesia
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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LEVEL AIR DENGAN SENSOR TINGKAT BERBASIS MIKROKONTROLER Achmad Ridwan
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 4 No. 2 (2021): Jutikomp Volume 4 Nomor 2 Oktober 2021
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v4i2.1905

Abstract

Level ketinggian air dengan sensor ketinggian yang bergantung pada mikrokontroler yang terdiri dari bagian dasar mikrokontroler 89S51. Perangkat ini dapat mengukur ketinggian air dengan menggambar garis ketinggian air yang ideal. Pilihan pengaturan ketinggian air diisolasi menjadi 4 alternatif, yaitu level 1, level 2, level 3, dan level 4. Pendekatan pembuatan perangkat dilengkapi dengan tahapan studi penulisan, perencanaan peralatan dan perencanaan pemograman. Konfigurasi peralatan termasuk rencana sirkuit sensor level, sirkuit kerangka mikrokontroler dasar, sirkuit penggerak lepas tangan untuk memutar dan menyalakan pompa air, serangkaian penunjuk level ketinggian dan sirkuit untuk membatasi tingkatan air. Siklus pengujian dilengkapi dengan tahapan reenactment yang berfungsi untuk menguji produk sebelum mengeksekusinya pada chip mikrokontroler. Hal ini dilakukan untuk membatasi cara mentransfer program pada chip mikrokontroler, karena jika dilakukan secara normal akan mempercepat kerusakan pada chip mikrokontroler. Hasil analisis instrumen ini telah berfungsi dengan baik sesuai tujuan dan pengaturan pengujian.
Debit Kalkulator Berbasis Android Achmad Ridwan
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 5 No. 1 (2022): Jutikomp Volume 5 Nomor 1 April 2022
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v5i1.2590

Abstract

Dalam menghitung debit air outlet ipal dilakukan dengan cara melihat berapa ketinggian air dipenampang pembuangan air ke badan sungai. Bentuk penampang pembuangan air ini ada yang berbentuk V (V-Notch) dan U (U-Notch). Ketinggian air pada penampang pembuangan air dicatat setiap jamnya kemudian dari pencatatan ketinggian tersebut disesuaikan atau dicocokan berapa debit air yang dihasilkan setiap jamnya pada tabel debit air outlet ipal. Pencatatan dan perhitungan ini dilaksanakan setiap hari dan dilaporan kepada atasan, pimpinan, atau yang bertanggung jawab setiap 1 minggu sekali. Akhirnya dalam pelaksanaannya terdapat kendala yaitu keakuratan perhitungan yang dilakukan dan waktu pelaporan yang cukup lama untuk disampaikan kepada atasan, pimpinan, atau yang bertanggung jawab. Untuk mengatasi kendala tersebut, dibuatlah sebuah aplikasi yang dapat menghitung dengan cepat dan akurat dan melaporkannya kepada atasan, pimpinan, atau yang bertanggung jawab yang dapat disesuaikan dengan kebutuhan pelaporan (maksimal 8 jam sekali). Aplikasi ini diberi nama Debit Kalkulator yang menggunakan sistem android. Dasar menggunakan sistem android ini dikarenakan petugas dan atasan, pimpinan, atau yang bertanggung jawab menggunakan smartphone/handphone yang menggunakan sistem android sehingga mempermudah pekerjaan dan sebagai penunjang pekerjaan karyawan yang bekerja.
Temu Bahaya & Pencemaran (TEBARAN) Berbasis Aplikasi Android Pada Sistem Sumbang Saran di PTNUSIRA,Untuk Mencegah Terjadinya Kecelakaan Kerja, Pencemaran Lingkungan Di Lingkungan Kerja Achmad Ridwan
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 5 No. 2 (2022): Jutikomp Volume 5 Nomor 2 Oktober 2022
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v5i2.2902

Abstract

Mencegah terjadi kecelakaan kerja atau pencemaran lingkungan pada lingkungan kerja disuatu perusahaan salah satu caranya dengan menemukan dan melaporkan dengan cepat dan detail temuan 5R, tindakan berbahaya, kondisi berbahaya dan pencemaran dilingkungan kerja tersebut. Temuan 5R, tindakan berbahaya, kondisi berbahaya dan pencemaran tersebut dengan cara melibatkan seluruh karyawan yang bekerja pada suatu perusahaan. Jika karyawan menemukan temuan 5R, tindakan berbahaya, kondisi berbahaya dan pencemaran membuat laporan temuan tersebut sehingga dapat ditindaklanjutin dengan melakukan tindakan pencegahan atau perbaikan agar tidak terjadinya kecelakaan kerja atau pencemaran dilingkungan kerja perusahaan. Selama ini karyawan menyampaikan temuan tersebut dengan cara menuliskan atau memprint temuan tersebut pada form laporan temuan dan lampirkan foto temuan, sehingga berdampak kepada karyawan malas atau ogah untuk menyampaikan temuan tersebut. Untuk mengantisipasi masalah ini maka dibuatkanlah sebuah aplikasi Temu Bahaya dan Pencemaran (TEBARAN) yang berbasiskan android. Menggunakan sistem android ini dikarenakan mempermudah karyawan membuat laporan jika menemukan temuan 5R, tindakan berbahaya, kondisi berbahaya dan pencemaran yang dapat menyebabkan terjadinya kecelakaan kerja atau pencemaran lingkungan. Metode yang digunakan dalam penelitian ini adalah wawancara dan praktek yang terkait pembuatan aplikasi tebaran yang berbasiskan android. Model yang digunakan dalam pembuatan aplikasi ini adalah model waterfall. Model ini digunakan karena pendekatan aplikasi secara berurut sehingga menghasilkan aplikasi yang sesuai dengan penerapan di PT Nusira. Berdasarkan hasil pengujian aplikasi tebaran berjalan baik sesuai yang diharapkan, sehingga dapat melibatkan seluruh karyawan serta dapat membuat laporan temuan dengan cepat untuk dapat ditindaklanjutin. Berdasarkan penelitian ini dapat diambil kesimpulan yaitu aplikasi tebaran berjalan baik sesuai target yang diharapkan serta aplikasi tebaran ini dapat digunakan pada perusahaan lain.
Pengembangan dan Pemanfaatan Aplikasi Literasi Digital Berbasis Android untuk Meningkatkan Kompetensi Mengajar Guru Amir Saleh; Fadhillah Azmi; Achmad Ridwan; M. Khalil Gibran
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3550

Abstract

Dalam era digital, guru perlu memiliki kompetensi pedagogis, kepribadian, profesional, dan sosial, termasuk kemampuan menggunakan teknologi. Sementara itu, pembelajaran berbasis teknologi di MTs. Al-Hijrah NU Medan belum sepenuhnya dilaksanakan karena berbagai kendala, seperti belum dimanfaatkannya aplikasi literasi digital dengan maksimal. Penelitian ini mengusulkan pengembangan aplikasi literasi digital untuk membantu guru dalam meningkatkan kemampuan mengajar dengan memanfaatkan teknologi dalam pembelajaran. Beberapa kendala yang ada terkait ketersediaan perangkat dan pemahaman guru tentang literasi digital. Pembelajaran literasi digital diperlukan untuk meningkatkan kemampuan guru dalam mengoperasikan teknologi karena hampir semua pembelajaran saat ini menggunakan media digital. Berdasarkan hasil implementasi aplikasi yang telah dikembangkan memperoleh hasil yang cukup baik, dimana memperoleh tingkat kepraktisan produk sebesar 83,13%. Sementara itu, penilaian yang diperoleh dari guru menunjukkan bahwa terdapat peningkatan sebesar 75% pada pengetahuan guru mengenai literasi digital dan peningkatan sebesar 81% pada kemampuan mereka dalam menerapkan literasi digital. Dari hasil perolehan nilai-nilai tersebut menyatakan bahwa pengembangan aplikasi yang dilakukan terbukti efektif dan mampu meningkatkan kemampuan mengajar guru.
Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes Azmi, Fadhillah; Gibran, M Khalil; Ridwan, Achmad
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3232

Abstract

In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.
Oven Listrik Keripik Buah Berbasis Arduino dengan Menggunakan Metode Fuzzy Logic dan Sensor DHT22 Achmad Ridwan
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4382

Abstract

Small-scale fruit chip production faces challenges in maintaining optimal temperature and humidity during the drying process. This research develops the Endull Kripps electric oven using Arduino-based fuzzy control technology and DHT22 sensors to address these issues. The aim is to design an efficient oven system that produces high-quality chips. The Research and Development method was applied in system design and testing. Results show up to 95% increase in energy efficiency compared to conventional methods. The chips produced have 1.5% lower moisture content, crispier texture, and higher organoleptic scores. The system maintains temperature stability with high accuracy, demonstrating significant potential in optimizing food drying processes. This innovation supports the Merdeka Belajar Kampus Merdeka program by providing a platform for students to develop entrepreneurial skills in the context of the food industry.
INTEGRATION SYSTEM OF THREE SECURITY FEATURES IN SMART DUAL MCB WITH AUTOMATIC LOAD BALANCING AND FIRE DETECTION BASED ON ARDUINO UNO (SINTAKS) Ridwan, Achmad; Prabowo, Agung
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.363

Abstract

The increasing use of electronic devices in Indonesian households has significantly strained traditional electrical systems, with electricity consumption growing 4.5% annually and 78% of urban homes utilizing over 10 electronic devices. This situation poses substantial fire risks, as electrical short circuits cause 62.8% of urban fires, with MCB overloads accounting for 27% of incidents. This research introduces SINTAKS (Sistem Integrasi Tiga Keamanan Smart Dual MCB), an innovative integrated system combining three essential safety features: energy monitoring, automatic load balancing, and early fire detection. Unlike conventional systems requiring separate components, SINTAKS provides a comprehensive solution using Arduino Uno as the main controller, integrated with ACS712 current sensors, DS18B20 temperature sensors, MQ-2 smoke detectors, and relay modules. The system demonstrates remarkable performance with 97.8% current measurement accuracy, load balance improvement from 62.7% to 91.3%, and fire detection response time of 2.9-4.7 seconds. Field testing in real household installations confirmed system reliability with 94.8% success rate across various operational scenarios. SINTAKS achieves 4.2% energy savings while maintaining cost-effectiveness at IDR 875,000, making it accessible for widespread residential implementation. This autonomous system operates independently without IoT dependence, ensuring reliable protection even in offline environments. The research successfully addresses critical gaps in household electrical safety through practical, affordable, and integrated technology.
Portable ECG Prototype based on Arduino and Random Forest Classification for Home Heart-Rate Monitoring Nugraha, R. Ferdy Akbar; Tindaon, Novendy Alberto Will; Susena, Arya; Duandes, Alfonso; Ridwan, Achmad
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3144

Abstract

Electrocardiogram (ECG) examination is essential for detecting heart rhythm disorders, yet limited access and high costs often prevent routine medical check-ups for many people. This study addresses these obstacles by designing and developing a portable ECG prototype capable of independent home-based heart monitoring. The system integrates an AD8232 sensor for signal acquisition, an Arduino Uno microcontroller as the main processor, and a simplified Random Forest classification algorithm to distinguish between normal, bradycardia, and tachycardia conditions. Measurement results are saved in CSV format on an SD card, then visualized and analyzed using Jupyter Notebook. The prototype was tested on 100 samples in a static and relaxed state to ensure signal stability. Its heartbeat classification achieved an accuracy of 99.0%, slightly higher than the PTB-XL reference dataset’s 98.0%, and consistent with results reported by recent TinyML- and Random Forest-based ECG studies. Unlike prior IoT-based frameworks, this work combines cost-effective microcontroller hardware with simplified offline on-device classification for practical daily monitoring without continuous cloud access. These findings confirm that the proposed system can produce reliable readings approaching clinical standards while remaining simple, affordable with a component cost under USD 31, and accessible for routine public heart health screening.
Comparison of C4.5 & Random Forest Based on AdaBoost For Determining Loan Eligibility Customer Funds Lenny, Lenny; Violyn, Violyn; Ridwan, Achmad; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14499

Abstract

This research discusses the comparison between two data mining algorithms, namely Decision Tree C4.5 and Random Forest based on AdaBoost, in determining the creditworthiness of customer funds. The main objective of this research is to evaluate and compare the performance of the two algorithms in predicting loan eligibility based on customer data. Algorithm performance is measured using accuracy, precision, recall, and misclassification error metrics. The research results show that the AdaBoost-based Random Forest is superior with an accuracy of 78.86%, recall of 98.75%, and the lowest misclassification error of 21.14%. Meanwhile, Decision Tree C4.5 provides lower performance than AdaBoost-based Random Forest. This research recommends further exploration of other algorithms, such as Support Vector Machine (SVM) and Neural Networks, to obtain more optimal results in determining customer loan eligibility.
IoT-Based Stress Monitoring Using CNN for HRV-GSR Analysis Segono, Yohandres; Hutagalung, Elia Yose Mayal; Simbolon, Harly Gumanti; Us, Nurul Iman; Ridwan, Achmad
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15671

Abstract

Stress has become a major global health concern affecting both physical and mental well-being. Conventional stress assessment methods rely on subjective self-reports that cannot capture real-time physiological changes. Existing systems are often limited to controlled laboratory environments or depend on traditional machine learning approaches requiring extensive manual feature engineering. This study aims to develop an Internet of Things–based stress monitoring system using deep learning to enable objective, continuous, and practical real-world stress detection.  The system incorporates wearable sensors using an ESP32-DevKit V1 microcontroller, a MAX30102 photoplethysmography sensor, and a Grove-GSR module for real-time acquisition of Heart Rate Variability and Galvanic Skin Response signals. A dual-branch Convolutional Neural Network architecture processes preprocessed HRV and GSR time-series data to automatically learn discriminative features without manual feature engineering. Data were collected from 30 participants, resulting in 8,100 labeled samples across four stress levels. The proposed CNN model achieved 91.3% classification accuracy, outperforming baseline machine learning models such as Support Vector Machine (78.4%), Random Forest (81.7%), and XGBoost (84.3%). Real-time system evaluation demonstrated an average latency of 1.47 seconds and battery endurance exceeding 13 hours, confirming the feasibility of continuous wearable stress monitoring. The integration of IoT infrastructure with deep learning provides an effective framework for physiological stress assessment, offering potential applications in preventive healthcare, workplace health management, and personalized mental-wellness monitoring.
Co-Authors Agung Prabowo Amir Saleh Duandes, Alfonso Fadhillah Azmi Fadhillah Azmi Fadhillah Azmi, Fadhillah Gibran, M Khalil Hutagalung, Elia Yose Mayal Lenny, Lenny M Khalil Gibran M. Khalil Gibran Nugraha, R. Ferdy Akbar Segono, Yohandres Sihombing, Yehezkiel Simangunsong, Putra Torang Simbolon, Harly Gumanti Susena, Arya Tindaon, Novendy Alberto Will Us, Nurul Iman Violyn, Violyn Yennimar, Yennimar