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Go Story: Design and Evaluation Educational Mobile Learning Podcast using Human Centered Design Method and Gamification for History Biabdillah, Fajerin; Tolle, Herman; A. Bachtiar, Fitra
Journal of Information Technology and Computer Science Vol. 6 No. 3: December 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (889.512 KB) | DOI: 10.25126/jitecs.202163345

Abstract

Technological developments, especially in the field of education, can help students learn more effectively and help the learning process. The learning method used in high school for history learning still uses conventional methods. The use of this conventional method often experiences problems such as students being less motivated in learning. One of the solutions proposed in this article is to design an android-based learning media that can support the activities of the learning process named go-story. Interface design for students as application users and (UI/UX) based on human centered design methodology and the concept of gamification. The human centered design approach and the concept of gamification will be used in the analysis and design process to maximize the usability and engagement of the application. The application will be implemented and tested on students to measure its effectiveness. The trials that have been carried out show the results of improvements
GROWTECH: PENYIRAMAN OTOMATIS BERBASIS INTERNET OF THINGS (IOT) MENGGUNAKAN NODEMCU V3 ESP8266 Ramadhan, Muhammad Cahyo Putra; Biabdillah, Fajerin; Wajiansyah, Agusma
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.7931

Abstract

Penelitian ini merancang dan mengimplementasikan sistem GrowTech berbasis Internet of Things (IoT) dengan NodeMCU V3 ESP8266 untuk meningkatkan efisiensi penyiraman tanaman. Sistem mengintegrasikan sensor kelembapan tanah, sensor suhu DS18B20, dan sensor kelembapan udara DHT11 yang terhubung ke aplikasi Blynk melalui Wi-Fi. Hasil pengujian menunjukkan sistem mampu memantau kondisi lingkungan tanaman secara real-time dan mengendalikan pompa air otomatis ketika kelembapan tanah turun di bawah 40%, serta menghentikan penyiraman saat mencapai 60–70%. Respon manual melalui aplikasi memiliki keterlambatan rata-rata hanya 1,2 detik. Implementasi pada skala rumah tangga terbukti efektif dalam mengurangi pemborosan air dan risiko kelebihan penyiraman. Potensi pengembangan diarahkan pada integrasi penyimpanan data berbasis cloud, algoritma prediksi kebutuhan air, serta penerapan pada skala perkebunan untuk mendukung pertanian cerdas dan berkelanjutan.
Perancangan dan Implementasi Fuzzy Inference System (FIS) Metode Tsukamoto pada Penentuan Penghuni Asrama Syahidi, Aulia Akhrian; Biabdillah, Fajerin; Bachtiar, Fitra Abdurrachman
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 1: Februari 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3214.757 KB) | DOI: 10.25126/jtiik.2019611228

Abstract

Asrama mahasiswa dibangun sebagai tempat tinggal bagi sekelompok orang yang sedang manjalankan suatu tugas atau kegiatan yang sama. Untuk menentukan mahasiswa yang berhak menjadi penghuni asrama, maka dalam penelitian ini memberikan rekomendasi untuk menggunakan Metode Fuzzy Tsukamoto. Metode Fuzzy Tsukamoto dipilih karena ada beberapa kelebihan yang menonjol yaitu dapat mendefinisikan nilai yang kabur dari inputan penilaian, dapat membangun, dan mengaplikasikan pengalaman-pengalaman para pakar secara langsung tanpa harus melalui proses pelatihan. Hasil analisis menyimpulkan bahwa: (1) Cara kerja Metode Fuzzy Tsukamoto memiliki tiga bagian yaitu: fuzzifikasi, inferensi fuzzy, dan defuzzifikasi, (2) Implementasi Metode Fuzzy Tsukamoto dapat menghitung penentuan penerimaan penghuni asrama mahasiswa pada studi kasus asrama mahasiswa putera “Negara Dipa Amuntai Malang”, berdasarkan 19 data dengan membandingkan antara hasil penilaian pakar, hasil perhitungan Fuzzy Tsukamoto secara manual, dan hasil perhitungan Fuzzy Tsukamoto secara otomatis menggunakan sistem yang terprogram, telah diuji mempunyai tingkat akurasi keberhasilan sebesar 63.15% dengan predikat cukup. AbstractStudent dormitory is built as a residence for a group of people who are carrying out a task or the same activity. To determine the students who have the right to become boarders, in this study provide recommendations for using the Fuzzy Tsukamoto Method. Fuzzy Tsukamoto method was chosen because there are several prominent advantages that can define the value that is blurred from the assessment input, can build, and apply the experiences of experts directly without having to go through the training process. The results of the analysis concluded that: (1) The workings of the Fuzzy Tsukamoto Method have three parts: fuzzification, fuzzy inference, and defuzzification. (2) The implementation of the Fuzzy Tsukamoto method can calculate the determination of the admission of students in the student dormitory case study of male student dormitory “Negara Dipa Amuntai Malang,” based on 19 data by comparing the results of the expert assessment, the results of Fuzzy Tsukamoto calculation manually, and the results of Fuzzy Tsukamoto calculations automatically using a programmed system, has been tested to have a success accuracy level of 63.15% with sufficient predicate. 
Designing a Web Application for Recognizing Past Learning Using the Laravel Framework Jaya, Arsan Kumala; Hanif, Abdullah; Triadi, Fara; Biabdillah, Fajerin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.239

Abstract

This study aims to provide information on the application design process using the Laravel framework. This study aims to design a web application that can help higher education institutions manage students who take prior learning recognition (RPL) classes effectively and efficiently. The problem often faced by universities is the difficulty in recording the formal/non-formal education history of RPL students. This application is expected to provide a solution by providing features such as recording education history, training history, conference history, award history, organizational history, and employment history. The system development method used in the design is the System Development Life Cycle (SDLC) by utilizing the Laravel framework as a framework for the system development process. The expected results of this study are a web application that is user-friendly, reliable, and able to increase the efficiency of student data collection in universities.
Impulsive Purchase with Vision Transformer Prediction of Vehicular Perception System for Fast-Food Outlets in Urban Traffic Congestion Biabdillah, Fajerin; Ismayanti, Rika; Hartanto, Subhan; Jaya, Arsan Kumala
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 4 (2025): October
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i4.53140

Abstract

Urban traffic congestion creates a unique environment where drivers are often captive audiences to roadside fast-food outlets and advertisements. This paper proposes a vision-driven impulsive purchase prediction system that simulates human-like vehicle vision using a Vision Transformer (ViT) model to detect fast-food outlet visibility, crowd levels, and promotional banner exposure in real-time. By integrating these visual cues, our system predicts the likelihood of impulsive stopping behavior (the “impulse score”) of drivers in heavy traffic. We collected and analyzed visual data from congested thoroughfares in major Indonesian cities (Jakarta, Surabaya, Bandung) known for severe traffic jams. The proposed ViT-based model was trained to identify key features such as recognizable outlet signage, drive-thru queue lengths, and promotional signage, mirroring the attention patterns of human drivers. Experimental results demonstrate that the model achieves high accuracy in detecting relevant cues and predicting impulsive purchase decisions, with a mean absolute percentage error (MAPE) of around 12% in forecasting impulse stop rates. This work is the first to leverage a transformer-driven computer vision approach for modeling consumer impulsivity in traffic, bridging automotive perception and marketing analytics. The findings suggest that smart vehicle systems and urban planners can benefit from such technology to anticipate consumer behavior in traffic, optimize roadside advertising, and manage congestion-related demand surges at fast-food outlets.
SMART AGRICULTURE: PEMANFAATAN SENSOR DHT11 BERBASIS INTERNET OF THINGS (IOT) UNTUK PEMANTAUAN SUHU DAN KELEMBAPAN UDARA Jeacfky Gozal Go, Aeltri; biabdillah, fajerin; Wajiansyah, Agusma
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8286

Abstract

Internet of Things (IoT) memberikan peluang besar dalam modernisasi pertanian melalui penerapan Smart Agriculture. Salah satu penerapannya adalah pemantauan suhu dan kelembapan udara yang berperan penting bagi pertumbuhan tanaman. Penelitian ini membahas pemanfaatan sensor DHT11 sebagai perangkat utama dalam sistem pemantauan berbasis IoT menggunakan NodeMCU ESP8266 dan platform Blynk untuk menampilkan data secara real-time. Tujuan penelitian ini adalah merancang sistem pemantauan lingkungan yang efisien, ekonomis, dan mudah diimplementasikan pada skala pertanian kecil. Hasil pengujian menunjukkan bahwa sistem mampu memantau suhu dan kelembapan udara secara real-time dengan waktu respons rata-rata 1–2 detik. Sensor DHT11 mendeteksi suhu antara 27,5°C–31,6°C dan kelembapan 60%–77% pada tiga periode pengujian (pagi, siang, malam), dengan akurasi ±0,3°C untuk suhu dan ±2% untuk kelembapan dibandingkan alat pembanding. Pada uji cuaca, suhu turun dari 31,6°C saat panas menjadi sekitar 25,8°C saat hujan, menandakan sensitivitas sensor yang baik terhadap perubahan lingkungan. Sistem Smart Agriculture berbasis IoT ini terbukti stabil, akurat, dan efisien dalam mendukung pemantauan kondisi lingkungan pertanian secara real-time serta berpotensi diterapkan pada otomatisasi pengelolaan lahan yang lebih cerdas.
AQUAMONITOR: SISTEM PEMANTAUAN KUALITAS AIR BERBASIS INTERNET OF THINGS (IOT) MENGGUNAKAN NODEMCU V3 ESP8266 Mulia, Abbizar; Biabdillah , Fajerin; wajiansyah, Agusma; Jeacfky Gozal Go, Aeltri
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8373

Abstract

Abstrak — Peningkatan pencemaran air menuntut adanya sistem pemantauan kualitas air yang efisien, akurat, dan mudah diakses. Penelitian ini mengembangkan AQUAMONITOR, yaitu sistem pemantauan kualitas air berbasis Internet of Things (IoT) yang menggunakan NodeMCU V3 ESP8266 sebagai pengendali utama. Sistem dirancang untuk mengukur dua parameter penting, yaitu suhu air menggunakan sensor DS18B20 dan Total Dissolved Solids (TDS) menggunakan Keyestudio TDS Meter V1.0. Data hasil pengukuran diproses dan dikirimkan secara real-time melalui jaringan Wi-Fi ke platform Blynk sehingga dapat dipantau melalui perangkat mobile. Hasil pengujian menunjukkan bahwa sistem mampu mengirimkan data dengan jeda kurang dari dua detik serta menjaga stabilitas koneksi selama pengujian 24 jam. Akurasi sensor juga berada pada tingkat yang baik, dengan selisih rata-rata ±0,3°C untuk suhu dan ±5 ppm untuk TDS, serta meningkat hingga 98–99% setelah proses kalibrasi. Dengan biaya implementasi rendah dan kinerja yang stabil, AQUAMONITOR berpotensi menjadi solusi monitoring kualitas air yang praktis, efisien, dan dapat dikembangkan lebih lanjut untuk mendukung pengelolaan sumber daya air secara berkelanjutan. Abstract — Water pollution continues to increase and requires an efficient, accurate, and easily accessible monitoring system. This study presents the development of AQUAMONITOR, an Internet of Things (IoT)-based water quality monitoring system using the NodeMCU V3 ESP8266 module as the main controller. The system is designed to measure two essential parameters: water temperature using the DS18B20 sensor and Total Dissolved Solids (TDS) using the Keyestudio TDS Meter V1.0. The acquired data is processed and transmitted in real-time via a Wi-Fi network to the Blynk platform, enabling users to monitor water conditions directly through mobile devices. Experimental results indicate that the system can transmit data with a response time of less than two seconds while maintaining stable connectivity during a 24-hour test period. Sensor accuracy is also satisfactory, with an average deviation of ±0.3°C for temperature and ±5 ppm for TDS, which further improves to 98–99% after calibration. With its low implementation cost, stable operation, and ease of use, AQUAMONITOR demonstrates strong potential as a practical and efficient water quality monitoring solution that can be further developed to support sustainable water resource management.
SMARTSOIL: SISTEM MONITORING KELEMBABAN TANAH BERBASIS INTERNET OF THINGS (IOT) MENGGUNAKAN ESP32 DAN SENSOR SOIL MOISTURE wa ode denada mar ella; Fajerin Biabdillah; Agusma Wajiansyah; Abbizar Mulia
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8788

Abstract

Abstrak. Kelembaban tanah merupakan parameter penting dalam menunjang pertumbuhan tanaman dan pengelolaan irigasi yang optimal. Namun, pemantauan kelembaban tanah yang masih dilakukan secara manual memiliki keterbatasan, seperti kurang akurat, tidak bersifat real-time, serta membutuhkan waktu dan tenaga yang lebih besar. Penelitian ini bertujuan untuk merancang dan mengimplementasikan SMARTSOIL, yaitu sistem monitoring kelembaban tanah berbasis Internet of Things (IoT) menggunakan mikrokontroler ESP32 dan sensor soil moisture. Metode penelitian meliputi perancangan sistem, pembuatan perangkat keras, pengembangan perangkat lunak, serta pengujian dan kalibrasi sensor. Sensor soil moisture digunakan untuk mendeteksi tingkat kelembaban tanah, kemudian data diproses oleh ESP32 dan ditampilkan melalui LCD OLED serta dikirimkan ke platform IoT untuk pemantauan jarak jauh secara real-time. Hasil pengujian menunjukkan bahwa sistem mampu membaca kelembaban tanah pada rentang 18%–82% sesuai kondisi tanah kering hingga basah, dengan kestabilan pembacaan yang baik dan waktu pengiriman data rata-rata 1,1–1,3 detik. Kesimpulannya, sistem SMARTSOIL mampu menyediakan data kelembaban tanah yang akurat dan real-time sehingga layak digunakan sebagai solusi monitoring kelembaban tanah berbasis IoT dalam mendukung pengambilan keputusan pengelolaan irigasi tanaman. Abstract. Soil moisture is an important parameter that plays a crucial role in supporting plant growth and optimal irrigation management. However, soil moisture monitoring that is still conducted manually has several limitations, including low accuracy, lack of real-time capability, and higher time and labor requirements. This research aims to design and implement SMARTSOIL, an Internet of Things (IoT)-based soil moisture monitoring system using an ESP32 microcontroller and a soil moisture sensor. The research methodology includes system design, hardware development, software development, as well as sensor testing and calibration. The soil moisture sensor is used to detect soil moisture levels, which are then processed by the ESP32 and displayed locally on an OLED LCD, as well as transmitted to an IoT platform for real-time remote monitoring. The experimental results show that the system is able to measure soil moisture levels in the range of 18%–82%, corresponding to dry to wet soil conditions, with stable readings and an average data transmission time of 1.1–1.3 seconds. In conclusion, the SMARTSOIL system is capable of providing accurate and real-time soil moisture data and is suitable to be used as an IoT-based soil moisture monitoring solution to support irrigation management decision-making.
Hybrid Regression–Simulation Model for Evaluating Emission Policies in Oversaturated Urban Corridors: A Case Study of Jakarta Fara Triadi; Arsan Kumala Jaya; Fajerin Biabdillah; Abdul Hanif
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10595

Abstract

Urban traffic emissions continue to escalate in Southeast Asian megacities, particularly along oversaturated central business district corridors where chronic congestion amplifies pollutant accumulation. Previous research often separates statistical emission modelling from microscopic simulation, limiting the ability to evaluate policy impacts under real-world saturation conditions. This study aims to assess whether lane-level transport interventions specifically bus-only lanes and motorcycle restrictions can reduce emissions in a hyper-congested Jakarta corridor through an integrated analytical approach. A hybrid regression–microsimulation framework was developed by combining multiple linear regression with SUMO-based traffic simulation. An hourly dataset of traffic flow and CO emissions (n = 8,760) from the Thamrin–Bundaran HI corridor was used to construct a regression model enriched with temporal and lagged predictors. The resulting emission profiles were embedded into SUMO to simulate baseline, bus-lane, and motorcycle-restriction scenarios. The regression model achieved strong predictive performance (R² = 0.692, RMSE = 0.252), with CO_lag1 confirmed as the dominant predictor. Simulation results showed fully overlapping CO₂ emission trajectories across all scenarios, indicating that lane-based interventions do not alter traffic states or emissions under oversaturated conditions. Structural congestion constrains the effectiveness of lane-level policies. Meaningful emission reductions require systemic strategies such as demand management, modal shift, or network redesign. The proposed hybrid framework provides a replicable tool for evaluating transport policies in dense urban corridors
Sentiment-Aware Transformer for Cryptocurrency Volatility Prediction Using Multi-Source Market and News Sentiment Fajerin Biabdillah; Fara Triadi; Aeltri Jeacfky Gozal Go; Muhammad Cahyo Putra Ramadhan
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10604

Abstract

The cryptocurrency market has grown into a multi-trillion-dollar domain with extreme volatility. This paper addresses the forecasting of crypto price movements and volatility by integrating market metrics with sentiment analysis. We identify a gap in existing studies, which often ignore multi-source sentiment and thus miss early warning signs of volatility. We propose a Sentiment-Aware Transformer model inspired by the Temporal Fusion Transformer (TFT). The model ingests daily price, volume, and market cap features from CoinMarketCap alongside aggregated sentiment scores from Twitter, Reddit, and financial news (extracted via FinBERT). We train and evaluate the model on 5 years of data for 10 major cryptocurrencies (2020–2024), comparing performance against LSTM and GRU baselines with identical inputs. The proposed Transformer achieves 83.2% volatility prediction accuracy with an F1-score of 0.81, exceeding the LSTM (79% accuracy) and GRU (80%) baselines. It also shows the lowest RMSE in price forecasting and a higher return correlation (0.72) with actual prices, indicating improved trend alignment. These gains are statistically significant (p<0.01). We also discuss how attention weights offer interpretability, as the model focuses on sentiment spikes during impending volatility.
Co-Authors Abbizar Mulia Abdul Hanif Aeltri Jeacfky Gozal Go Agus Triyono Agusma Wajiansyah Agusma Wajiansyah, Agusma Arsan Kumala Jaya Dwi Titi Maesaroh Fara Triadi Fara Triadi Fitra Abdurrachman Bachtiar Hanif, Abdullah Hartanto, Subhan Herman Tolle Ismayanti, Rika Jauharatul Maknuniah Jeacfky Gozal Go, Aeltri Jonathan Raimundus Muhammad Awaluddin A. Muhammad Cahyo Putra Ramadhan Muhammad Tajuddin Mulia, Abbizar Nur’ Aini Ramadhan, Muhammad Cahyo Putra Syahidi, Aulia Akhrian wa ode denada mar ella