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All Journal Jurnal Teknik Elektro JTEIN: Jurnal Teknik Elektro Indonesia Jurnal Teknik Elektro: Ilmu dan Teknologi
Iwan Handoyo Putro
Electrical Engineering Department, Faculty of Industrial Technology, Petra Christian University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Performance of Various Codecs Related to Jitter Buffer Variation in VoIP Using SIP Putro, Iwan Handoyo
Jurnal Teknik Elektro Vol 8, No 2 (2008): SEPTEMBER 2008
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (144.938 KB) | DOI: 10.9744/jte.8.2.103-108

Abstract

Briefly speaking, there are two popular Voice over Internet Protocol (VoIP) standards, H.323 and Session Initiation Protocol (SIP). The first standard was designed by ITU and has become the basis for the widespread implementation of VoIP systems although it was not specifically designed for it. The second standard, SIP, was proposed by IETF and it is designed to connect, communicate and exchange data with the internet applications. In order to deliver voice conversation through packet-switching networks, codecs (coder-decoder) should be implemented to compress and later decompress those packets. In this paper, some of compression algorithms will be compared and analyzed based on its performances in SIP based VoIP system. The codecs that was used in this experiment are SJ Lab GSM 6.10, SJ Lab iLBC-30ms, SJ Lab iLBC-20ms, Microsoft CCITT G.711 A-law and Microsoft CCITT G.711 u-law. These codecs are tested in terms of its ability to deal with jitter buffer variations. The result shows that SJ Lab iLBC-20ms gives the best performance in terms of jitter buffer variation on LAN environment while SJ Lab GSM 6.10 shows the highest performance on wireless networks testing.
Kebun Pintar dalam Ruang Berbasis Internet of Things Ardiansyah, Vicky; -, Thiang; Putro, Iwan Handoyo
Jurnal Teknik Elektro Indonesia Vol 5 No 2 (2024): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v5i2.658

Abstract

The concept of smart farming emerged as a sign that technology developments in the agricultural sector are growing rapidly. Home gardening activities have become a trend during the pandemic and are often still done manually. This research aims to create a prototype of an indoor smart garden that is connected via the Blynk application to control and display sensor readings. The plant used as a test was a peppermint plant with a testing period of 4 weeks with 2 different treatments. Based on the results of the overall system testing that has been carried out, the monitoring and control system can work well. The soil moisture sensor is prone to corrosion, so it needs to be replaced every 6-7 days. Peppermint plants, in the second test which were given a set point of soil moisture of 60% and provided light for 23 hours, experienced an increase in plant height, the color of the leaves is green and number of leaves increases when compared to the first test which was given a set point of soil moisture of 50% and provided light for 9 hours.
SIMULASI KONTROL KELEMBABAN DENGAN KONTROLER PID PADA KUMBUNG JAMUR DI URBAN FARMING ALAM SARI PETRA Kufa, Sih Kawuryan Yulianes; Lim, Resmana; Santoso, Petrus; Putro, Iwan Handoyo
Jurnal Teknik Elektro Vol. 17 No. 1 (2024): Maret 2024
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jte.17.1.22-28

Abstract

Urban farming adalah kegiatan bercocok tanam atau beternak secara mandiri di wilayah perkotaan dengan memanfaatkan lahan yang terbatas atau kosong. Urban Farming Alam Sari Petra di pusat kota Surabaya bertujuan untuk mendidik pendidik dan peserta didik tentang pertanian perkotaan dan tanaman organik. Salah satu kegiatan yang dilakukan adalah budidaya jamur tiram. Namun, pengoperasian manual sistem penyiram di kumbung jamur menyebabkan kelembaban yang tidak terkontrol dan pertumbuhan jamur yang tidak optimal. Untuk mengatasi hal ini, diimplementasikan sebuah sistem pengatur kelembaban menggunakan kontroler PID. Permasalahan penelitian ini difokuskan pada bagaimana mempertahankan kelembaban pada tingkat tertentu selama masa inkubasi dan masa panen di kumbung jamur. Pengujian yang dilakukan adalah tuning pid, pengujian kontrol tanpa gangguan, pengujian kontrol dengan gangguan dan pengujian kontrol dengan target kelembaban berbeda. Tahapan pengujian ini bertujuan untuk mengevaluasi kinerja dari sistem. Berdasarkan hasil pengujian, dapat disimpulkan bahwa penggunaan kontroler PID dalam mengontrol kelembaban hingga berhasil mencapai tingkat kelembaban yang stabil dan sesuai dengan set point yang diminta.
PERANCANGAN DAN PEMANTAUAN LOCKER MENGGUNAKAN KTM BERBASIS ESP8266 Rory, Daniel Ignatius; Putro, Iwan Handoyo; Khoswanto, Handry
Jurnal Teknik Elektro Vol. 17 No. 1 (2024): Maret 2024
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jte.17.1.36-43

Abstract

Penelitian ini dilatarbelakangi oleh beberapa kasus dimana barang atau komponen hilang saat di rak lab kampus, beberapa kasus dimana salah satu proyek dari mahasiswa rusak karena ditinggal di rak lab kampus, ada juga beberapa kasus dimana barang yang dititipkan di lab tertinggal atau lupa akan barangnya. Hal ini menyebabkan kerugian pada pengguna rak serta kurangnya informasi asisten lab akan barang tersebut. Pada studi ini, penulis berusaha untuk membuat alat yang bisa berfungsi sebagai sistem keamanan locker dengan menggunakan RFID pada KTM mahasiswa. Alat ini bisa memantau penggunaan locker user, locker nomor berapa yang digunakan dan jam penggunaan locker. Proses pemantauan ini bisa lakukan oleh admin ataupun user itu sendiri. Pengujian dilakukan dengan 3 metode. Pertama, Pengujian hardware yang terdiri dari pengujian alat RFID tanpa dan menggunakan media penghalang. Kedua, pengujian software yang terdiri dari pengujian input, edit, tambah, hapus dan pengujian melakukan monitoring. Ketiga, pengujian sistem secara keseluruhan.
CYBERSECURITY IN SMART HEALTHCARE: A MACHINE LEARNING APPROACH Putro, Iwan Handoyo
Jurnal Teknik Elektro Vol. 18 No. 1 (2025): Maret 2025
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jte.18.1.40-45

Abstract

The adoption of Internet of Things (IoT) technologies in medical devices has greatly enhanced healthcare capabilities. This enables continuous patient monitoring, real-time diagnostics, and remote care. However, this connectivity also introduces significant cybersecurity threats that can compromise patient safety and system integrity. This paper presents a machine learning-based framework for detecting threats in IoT-enabled medical devices. This study utilizing the WUSTL-EHMS-2020 dataset that taking a collection of network traffic from real-world healthcare IoT systems. A comparative evaluation of multiple classifiers was conducted to assess detection effectiveness and computational efficiency. In terms of accuracy value, the Decision Tree (DT) achieves highest value of 0.97. The Random Forest (RF) model demonstrated more optimum performance across metrics with accuracy at 0.94, precision of 0.95, recall of 0.56, and F1-score of 0.70. Meanwhile, XGBoost (XGB) achieved the highest Area Under the Curve (AUC) score at 0.95, indicating strong overall classification performance. Conversely, Gaussian Naive Bayes (GNB) exhibited the weakest results, with an accuracy of 0.86, F1-score of 0.46, and the lowest AUC score of 0.73. Notably, K-Nearest Neighbors (KNN) achieved the fastest training time of just 0.001 seconds, offering a preferable option for deployment in time-sensitive environments. These results highlight the trade-offs between accuracy, speed, and robustness in machine learning-based intrusion detection systems. This study underscores the potential of intelligent threat detection models in strengthening the security of modern medical IoT infrastructures, all while balancing computational constraints.
AIR QUALITY PREDICTION USING IOT AND MACHINE LEARNING Putro, Iwan Handoyo
Jurnal Teknik Elektro Vol. 19 No. 1 (2026): Maret 2026
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jte.19.1.%p

Abstract

Air pollution has become a critical environmental and public health concern, particularly in urban areas where industrial activities and transportation contribute significantly to particulate matter emissions. The emergence of Internet of Things (IoT) technologies has enabled continuous and real-time monitoring of environmental conditions through distributed sensor networks. However, raw sensor data alone is insufficient without intelligent analysis for accurate forecasting and decision-making. This study proposes a machine learning-based approach for air quality prediction using IoT-derived environmental data. The Beijing PM2.5 dataset was utilized to simulate real-world IoT sensor measurements, incorporating meteorological and temporal features. Three machine learning models: Linear Regression, Random Forest, and Gradient Boosting were implemented and evaluated using standard performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). Experimental results indicate that the Random Forest model achieved the best performance, with an RMSE of 47.05, and R² score of 0.75. In comparison, Gradient Boosting produced an RMSE of 66.27 and R² of 0.50, while Linear Regression showed the lowest performance with an RMSE of 80.14 and R² of 0.27. These results demonstrate that tree-based ensemble methods, particularly Random Forest, are more effective in capturing the nonlinear relationships present in environmental data. This work highlights the potential of integrating IoT sensing with machine learning models to support accurate air quality prediction and informed environmental management
Co-Authors -, Thiang Ardiansyah, Vicky Handry Khoswanto Kufa, Sih Kawuryan Yulianes Petrus Santoso Resmana Lim Rory, Daniel Ignatius