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DETEKSI API MENGGUNAKAN BACKGROUND SUBSTRACTION DAN ARTIFICIAL NEURAL NETWORK UNTUK REAL TIME MONITORING Andi Kamaruddin; Vincent Suhartono; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 1 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.01 KB)

Abstract

The most important initial step in the detection and localization of the fire is to detect fire quickly and reliably. Video-based surveillance is one of the most promising solutions for automatic fire detection with the ability to monitor a large area and ease of reading an alarm to the operator through the monitorSupervision, unfortunately, the main drawback of video-based fire monitoring system that uses optic is a false alarm caused by an Error detection (Error detection), for it is then in this study using the feature extraction GLCM (Gray level Coocurance Matrix) as input spectral classification of Neural network to detect fire, the approach can reduce the Average Error detection with Error detection rate Average is 7%
Prediksi Jumlah Persediaan Telur Ayam Menggunakan Metode K-Neares Neighbor: - baguna, filda; Husdi, Husdi; Taliki, Sunarto; Kamaruddin, Andi
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 2 (2023): November 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i2.119

Abstract

Abstract - UD. Unggas Karya Mandiri is one of the production areas producing chicken eggs in Banggai Laut Regency, with the existence of UD Unggas Karya Mandiri Banggai Laut which has the aim of increasing the number and types of employment opportunities for Banggai Laut Regency in particular. Based on the results of research at UD Unggaas Karya Mandiri Banggai Laut, this is the result of fluctuating chicken egg production or unstable production which is caused by several things, namely lack of availability of feed ingredients, anti-biotic drugs, vaccinations and so on. The aim of this research is to obtain better accuracy in predicting the number of chicken egg supplies at UD Unggas Karya Mandiri Banggai Laut by applying the K-Nearest Neighbor method. Based on the prediction results with existing data, it was obtained using the K-Nearest Neighbor method. This application was able to predict the number of chicken egg supplies at UD Unggas Karya Mandiri. It can be seen that the prediction application for the number of chicken egg supplies at UD Unggas Karya Mandiri Banggai Laut can be applied with an accuracy of 96.98% of the prediction results obtained.. Keywords: Prediction, Eggs, Accuracy, Inventory, K-Nearest Neighbor
Algoritma Linear Regresi Untuk Prediksi Ketimpangan Pendapatan Berdasarkan Gini Ratio Di Provinsi Gorontalo Husdi; Kamaruddin, Andi
KETIK : Jurnal Informatika Vol. 2 No. 03 (2025): Januari
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/ketik.v2i03.130

Abstract

Ketimpangan pendapatan antar kelompok dapat diukur dengan menggunakan Indeks Gini (Gini Ratio). Indeks Gini dapat bernilai antara 0 hingga 1, dimana semakin kecil/ semakin angka indeks mendekati 0 berarti pendapatan antar kelompok semakin kecil (pemerataan sempurna), sedangkan semakin besar angka indeks/semakin angka indeks mendekati 1 berarti semakin tinggi disparitas pendapatan penduduk di wilayah tersebt. Dalam kurun waktu 5 tahun terakhir, angka indeks gini di Provinsi Gorontalo cenderung stabil pada angka 0,406–0,418. Indeks Gini sempat meningkat pada tahun 2022 namun kembali menurun di tahun 2023 pada angka 0,417. Permasalahan dalam penelitian ini adalah bagaimana cara mengetahui (Gini Ratio) di Provinsi Gorontalo untuk tahun Berikutnya. Penelitian ini menggunakan metode penelitian jenis experimen dengan Subjek penelitian ini adalah prediksi Ketimpangan Pendapatan Antar Kelompok Di Provinsi Gorontalo. Metode Regresi Linear Sederhaan dapat digunakan untuk memprediksi Ketimpangan Pendapatan Berdasarkan Gini Rasio Antar Kelompok Di Provinsi Gorontalo secara tepat dan akurat, hal ini berdasrkan dari hasil pengujian dan mendapatkan nilai MAPE 0,83 % dengan interpretase mape kategori Sangat tepat/ kemampuan peramalan Sangat baik
Integrasi Long Range (LORA) Technology dan Message Queuing Telemetry Transport (MQTT) pada Aplikasi Mitigasi Banjir Andi Kamaruddin; Husdi; Yusuf Ksatria Ilham Andi Hasan; Aril
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Kota Gorontalo merupakan salah satu wilayah yang rentan terhadap bencana banjir akibat kondisi hidrologis yang dilalui oleh beberapa sungai besar. Data historis menunjukkan bahwa banjir yang terjadi pada Juli 2024 merupakan salah satu kejadian terparah dalam satu dekade terakhir. Berbagai sistem mitigasi banjir berbasis Internet of Things (IoT) telah dikembangkan, namun sebagian besar masih bergantung pada konektivitas internet sehingga kurang optimal diterapkan pada daerah yang memiliki keterbatasan jaringan komunikasi. Penelitian ini bertujuan mengembangkan aplikasi mitigasi banjir dengan mengintegrasikan teknologi Long Range (LoRa) dan Message Queuing Telemetry Transport (MQTT) untuk mendukung transmisi data sensor secara real-time pada area yang memiliki maupun tidak memiliki akses internet. Metode yang digunakan adalah eksperimen dengan memanfaatkan sensor ultrasonik waterproof, water flow sensor, dan piezo vibration sensor yang terhubung pada arsitektur LoRa-MQTT. Pengujian dilakukan terhadap kemampuan sistem dalam membaca parameter banjir serta kualitas komunikasi data berdasarkan parameter delay dan packet loss. Hasil pengujian menunjukkan bahwa sistem mampu mengirimkan data sensor pada jarak hingga 2000 meter dengan rata-rata delay sebesar 0,412 detik dan packet loss sebesar 0%. Sistem juga berhasil mengklasifikasikan kondisi banjir ke dalam status aman, waspada, dan bahaya berdasarkan hasil pembacaan sensor secara real-time. Hasil tersebut menunjukkan bahwa integrasi LoRa dan MQTT mampu menyediakan komunikasi data yang andal, menjaga keutuhan data selama transmisi, serta mendukung sistem peringatan dini banjir yang dapat diakses melalui aplikasi web dan mobile.   Abstract Gorontalo City is one of the regions vulnerable to flood disasters due to its hydrological conditions, which are influenced by several major rivers flowing through the area. Historical data indicate that the flood event in July 2024 was among the most severe disasters recorded over the past decade. Various Internet of Things (IoT)-based flood mitigation systems have been developed; however, most of them rely heavily on internet connectivity, making them less effective in areas with limited communication network infrastructure. This study aims to develop a flood mitigation application by integrating Long Range (LoRa) technology and the Message Queuing Telemetry Transport (MQTT) protocol to support real-time sensor data transmission in areas with and without internet access. The research employed an experimental method using a waterproof ultrasonic sensor, water flow sensor, and piezo vibration sensor integrated within a LoRa-MQTT architecture. System evaluation was conducted by measuring its capability to monitor flood-related parameters and assessing communication performance based on delay and packet loss metrics. The results show that the proposed system successfully transmitted sensor data over distances of up to 2,000 meters, achieving an average delay of 0.412 seconds and a packet loss rate of 0%. The system was also able to classify flood conditions into safe, alert, and danger levels based on real-time sensor readings. These findings demonstrate that the integration of LoRa and MQTT provides reliable data communication, ensures data integrity during transmission, and supports a flood early warning system accessible through both web and mobile applications.