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All Journal International Journal of Advances in Intelligent Informatics International Journal of Artificial Intelligence Research Jurnal Teknologi Informasi dan Komunikasi
Yuli Murdianingsih, Yuli
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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SISTEM KONTROL LAMPU TERAS RUMAH BERBASIS IoT (Internet of Things) MENGGUNAKAN METODE NAÏVE BAYES PADA PLATFORM NODE-RED Aulia, Regita Faza; Murdianingsih, Yuli; Faizal, Muhammad; Suryadi, Usep Tatang; Abidin, Aa Zezen Zaenal
Jurnal Teknologi Informasi dan Komunikasi Vol 17 No 2 (2024): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v17i2.271

Abstract

Salah satu energi yang banyak digunakan sehari-hari adalah energi untuk penerangan. Setiap rumah pasti memiliki sumber penerangan, salah satu sumber penerangan yang ada di rumah adalah lampu. Saat penghuni rumah berpergian seringkali lampu teras rumah dibiarkan menyala. Untuk mengatasi hal tersebut maka diperlukan sebuah pengendali pada lampu teras rumah agar lebih efisien lampu mati saat matahari terbit, menyala saat cuaca mendung dan saat malam hari agar mencegah tindak kriminalitas saat penghuni rumah sedang berpergian. Dengan dibuatnya sistem kontrol lampu teras rumah berbasis Internet of Things menggunakan metode Naïve Bayes pada platform Node-RED, pemilik rumah dapat mengontrol lampu teras rumahnya dari jauh. Sistem ini memanfaatkan NodeMCU untuk menyambungkan data ke platform menggunakan jaringan internet, sensor LDR yang berfungsi mendeteksi intensitas cahaya sekitar, sensor DHT11 untuk mendeteksi suhu dan kelembaban disekitar, modul relay untuk menyambungkan lampu dengan perangkat lainnya, dan platform Node-RED yang akan mengontrol lampu teras rumah.
Machine Learning Penyortiran Buah Naga Menggunakan Algoritma K-Means Berbasis Internet of Things Menggunakan Platform Blynks Ramadan, Wanda; Abidin, Aa Zezen Zenal; Suryadi, Usep Tatang; Murdianingsih, Yuli; Faizal, Muhammad; Suhendri, Suhendri; Carkiman, Carkiman
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 1 (2025): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i1.285

Abstract

Salah satu tahapan dalam proses pengelolaan hasil pertanian dan perkebunan ialah dengan melakukan pembagian terstruktur mengenai produk untuk menentukan kualitas hasil panen. Penyortiran dilakukan dengan melihat kualitas rona kulit, berat buah serta mengetahui jumlah satu kali panen. Kualitas buah naga ditentukan oleh berbagai parameter, antara lain umur dan kematangan (indeks warna), ukuran, dan berat buah. Sebagai salah satu komoditas yang disukai banyak orang, buah naga memerlukan proses sortasi (seleksi), karena pasar membutuhkan kondisi keseragaman buah naga. Seleksi biasanya dilakukan menurut prinsip pemisahan, seperti: bobot yang berbeda, bentuk yang berbeda, sifat permukaan yang berbeda, berat jenis yang berbeda, tekstur warna yang berbeda dan kematangan yang berbeda. Dalam proses penyortiran manual, manusia memiliki kelemahan dalam melakukan tugas sensorik dengan kapasitas besar dan jam kerja yang Panjang. Berangkat dari permasalahan tersebut penulis tertarik untuk membuat alat yaitu Machine Learning Penyortiran Buah Naga Berbasis Internet of Things Menggunakan Algoritma K- Means Pada Platform Blynk. Metodologi yang digunakan penulis diantaranya Studi pustaka, dokumentasi, data mining, analisa sistem, perancangan sistem, pembuatan sistem, pengujian sistem. Machine Learning Penyortiran Buah Naga Berbasis Internet of Things Menggunakan Algoritma K- Means Pada Platform Blynk yang penulis kerjakan dapat berhasil terealisasikan menggunakan sensor Load Cell untuk menghitung berat dan sensor TCS230 untuk menentukan warna. Serta sensor TCS3200 dapat mendeteksi warna dengan baik. Data yang didapat oleh alat dapat diklasterisasi menggunakan Algoritmaa K-Means dengan benar sebanyak 7 iterasi dengan nilai BCV=2096,84, WCV=442563,35, Rasio=211.
A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system Abidin, Aa Zezen Zaenal; Othman, Mohd Fairuz Iskandar; Hassan, Aslinda; Murdianingsih, Yuli; Suryadi, Usep Tatang; Siallagan, Timbo Faritchan
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1026

Abstract

Knowledge Extraction Model (KEM) is a system that extracts knowledge through an IoT-based smart waste bin emptying scheduling classification. Classification is a difficult problem and requires an efficient classification method. This research contributes in the form of the KEM system in the classification of scheduling for emptying waste bins with the best performance of the Machine Learning method. The research aims to compare the performance of Machine Learning methods in the form of Decision Tree, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Multi-Layer Perceptron, which will be recommended in the KEM system. Performance testing was performed on accuracy, recall, precision, F-Measure, and ROCS curves using the cross-validation method with ten observations. The experimental results show that the Decision Tree performs best for accuracy, recall, precision, and ROCS curve. In contrast, the K-NN method obtains the highest F-measure performance. KEM can be implemented to extract knowledge from data sets created in various other IoT-based systems.
IoT-Based Guppy Aquaculture Monitoring System Using C 4.5 Method on Thingspeak Platform abidin, aa zezen zaenal; Murdianingsih, Yuli; Ruhiyat, Ilham; Suryadi, Usep Tatang; Iskandar Othman, Mohd. Fairuz; Faizal, Muhammad
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (712.932 KB) | DOI: 10.29099/ijair.v6i2.387

Abstract

Monitoring water media in Guppy fish farming is a major problem that must be solved. monitoring is carried out to obtain a decision whether the media is suitable or not suitable for getting good guppy fish.This study aims to extract knowledge in order to make decisions on the quality of Guppy fish water media through data obtained from the IoT system.The main contribution of this research is the effort to obtain new knowledge from data collected through IoT systems. New knowledge is obtained from water quality parameter data acquired by sensors of temperature, water level and pH. data from the sensor is sent to the Thingspeak cloud application via the microcontroller module. Data from the cloud is extracted into new knowledge in the form of decision-making rules for the quality of Guppy fish water media. To validate the method used, an analysis was performed using a confusion matrix in the rapidminer application. tested for the C 4.5 method and the Naive Bayes methodThe results obtained the same high accuracy of 100 percent. It is possible that this IoT system can be applied in a larger scope, for example monitoring the aquariums of various Guppy fish farming communities in a city, so that real time data on the quality of Guppy fish is obtained within the scope of Smart City.
Optimalisasi Sistem Pemilu Melalui Implementasi E-Voting Berbasis Blockchain Dengan Keamanan Kriptografi AES-128 Suryadi, Usep Tatang; Permana , Ardi; Abidin, Aa Zezen Zaenal; Murdianingsih, Yuli; Carkiman, Carkiman; Faizal, Muhammad
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 2 (2025): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i2.343

Abstract

The general election (Pemilu) is a fundamental element of a democratic system. This study develops and evaluates a blockchain-based e-voting system implementing AES-128 encryption to ensure the confidentiality, integrity, and availability of voting data. The system integrates AES-128 symmetric encryption for data at-rest and SHA-256 hashing at the blockchain layer. Testing was conducted on a simulated dataset containing 100,000 voting records to measure processing time, storage efficiency, and cryptographic resilience against brute-force and data manipulation attacks. Experimental results show an average read/processing time of 24 seconds for 100,000 records under the test server configuration, and theoretical security analysis indicates that brute-forcing AES-128 is impractical with current computational capabilities. The contribution of this research lies in the integrated design of an e-voting system that combines data encryption and distributed storage models with verification mechanisms, thereby enhancing the transparency and auditability of the election process.
Co-Authors Aa Zezen Zaenal Abidin, Aa Zezen Zaenal Abidin, Aa Zezen Zenal Aulia, Regita Faza Carkiman, Carkiman Hassan, Aslinda Iskandar Othman, Mohd. Fairuz Muhammad Faizal Othman, Mohd Fairuz Iskandar Permana , Ardi Ramadan, Wanda Ruhiyat, Ilham Siallagan, Timbo Faritchan Suhendri Suhendri Usep Tatang Suryadi, Usep Tatang