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Olivia Kembuan
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oliviakembuan@unima.ac.id
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jointer@unima.ac.id
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Program Studi Teknik Informatika Fakultas Teknik, Kampus Unima di Tondano, Minahasa, Sulawesi Utara
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JOINTER : Journal of Informatics Engineering
Published by Universitas Negeri Manado
ISSN : -     EISSN : 27237958     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Journal of Informatics and Engineering (Jointer) diterbitkan oleh Program Studi Teknik Informatika, Fakultas Teknik (FATEK) Universitas Negeri Manado (UNIMA) setiap bulan Juni dan Desember dengan nomor e-issn : 2723-7958. Jointer merupakan jurnal open-access atau dengan kata lain semua artikel yang diterbitkan bersifat terbuka dan dapat diakses tanpa biaya untuk mendukung pertukaran pengetahuan secara global. Jointer menerbitkan artikel penelitian (research article), artikel telaah/studi literatur (review article/literature review), laporan kasus (case report) dan artikel konsep atau kebijakan (concept/policy article), di bidang-bidang menyangkut Teknologi Informasi seperti berikut : Business Process Management Business Intellegent Computer Architecture Design Computing Theory Conceptual Modeling, Languages and design Computer Network Data Mining Data Warehouse Decision Support System e-Healthcare, e-Learning, e-Manufacturing, e-Commerce Embedded system Enterprise Application ERP dan Supply Chain Management Geographical Information System Human Computer Interaction Image Processing and Pattern Recognition Information Infrastructure for Smart Living Spaces Information Retrievel Information Security Information-centric Networking Intelligent Transportation Systems IT Management dan IT Governance Media, Game and Mobile Technologies Models, Methods and Techniques Natural Language Processing Network Computer Security Remote Sensing Robotic Systems Smart Appliances & Wearable Computing Devices Smart City Smart Cloud Technology Smart Sensor Networks Smart Systems Software Engineering
Arjuna Subject : Teknik (semua kategori) - Teknologi Media
Articles 5 Documents
 1 
Search results for , issue "Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering" : 5 Documents clear
Metode Latent Dirichlet Allocation dalam Pemodelan Topik Headline Berita Online tentang Hukum dan Kriminal Medea, Mega Jayanti; Rantung, Vivi Peggie; Kembuan, Olivia
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.63

Abstract

Online news is a report about an event that is presented online by a news publishing company through an online news portal. Manado News is a news portal that presents news online which is divided into several categories. This study aims to analyze the data on news titles on the Manado News online news portal, Legal and Criminal category, to find out the crimes that are rampant and reported on this news portal. The analysis is carried out by implementing one of the methods in topic modeling, namely Latent Dirichlet Allocation. This method is used to find the topic of discussion in a document with a large capacity and displays the words that appear the most in a topic. The data needed for modeling was obtained by scraping on the online news portal Manado News, with a span of five years, from April 2017 to April 2022. After the data was processed using the Latent Dirichlet Allocation method, one topic of discussion was found, namely conventional crime groups. The topic contains a number of words which are types of crime in the conventional crime category, namely, persecution, theft, murder, stabbing, motor vehicle theft, sexual abuse and liquor.
Analisis Performa Autoregressive Integrated Moving Average Model dan Deep Learning Long Short-Term Memory Model untuk Peramalan Data Cuaca Montolalu, Vithiaz; Munaiseche, Cindy; Krisnanda, Made
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.112

Abstract

Weather is an aspect that cannot be separated from all activities carried out by humans, so information about the weather is very important. To meet the need for this information, it is necessary to do forecasting. Each data has its own characteristics, and choosing the right forecasting method is very important. The Autoregressive Integrated Moving Average (ARIMA) method is one of the popular statistical methods used in forecasting time-series data. Long Short-Term Memory (LSTM) is a modern deep learning algorithm model that is most suitable for forecasting time-series data. In this study, an analysis was carried out to compare the traditional ARIMA method and the deep learning model, namely LSTM, in forecasting weather data in Manado city to see the best forecasting model that can be used. The results of this study indicate that in terms of the accuracy of the 18 tests performed, the LSTM forecasting model is superior to the ARIMA model as measured by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). In terms of computational time in making forecasting models for 6 weather data attributes, the LSTM model is faster than the ARIMA model.
Klasifikasi Kesegaran Ikan Menggunakan Citra Mata dengan Convolutional Neural Network Arsitektur VGG-16 Ni Made Sri Ulandari; Resti Ajeng Sutiani; Rizal Adi Saputra
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.350

Abstract

Sea fish are the most widely consumed type of fish by households in Indonesia, serving as an important source of protein for the body. According to Matondang's (2022) study titled "Comparison of Protein Content in Freshwater Fish and Sea Fish," the protein content in sea fish is higher than in freshwater fish, making high-quality fish highly beneficial for the body. The fishing industry plays a crucial role in food supply, especially in maritime countries like Indonesia. The freshness of sea fish, as the main protein source for many households, significantly determines its quality and safety for consumption. Freshness affects nutritional value, taste, and prevents health risks from consuming stale fish. This study employs the Convolutional Neural Network (CNN) method with the VGG-16 architecture to classify fish freshness based on eye images. The dataset used consists of 1,903 fish eye images, augmented to 4,560 images. Classification results indicate that the VGG-16 model can distinguish between fresh and stale fish eyes with an accuracy of 85.26%. This research is expected to assist the fishing industry in monitoring fish quality more effectively and efficiently, as well as enhancing the safety of fish consumption for the community.
Sistem Pemantauan Cuaca Berbasis Internet of Things Phang, Alvaro D.; Sitanayah, Lanny; Sanger, Junaidy B.
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.366

Abstract

Weather is one of the important parts that occur in human life. The fields of human occupation that are most affected by the weather, for example, are agriculture, tourism, transportation, and also other fields that require outdoor activities. Before working, humans will see the weather forecast through various media such as weather applications. When entering the rainy season, the weather in Indonesia cannot be predicted accurately even though it has been forecasted, because the weather in Indonesia during the rainy season can change. Currently, Indonesia has used the AWS system but it is still in limited numbers which is also because the AWS used is still made by another country, so it has a fairly expensive price. Therefore, this study will design and implement an Internet of Things-based Weather Monitoring System using the Naïve Bayes algorithm that can help classify the weather status where the device is located. The device is built using DHT11 sensors, LDR, and rain gauge sensors as input parameters for weather data. Based on the tests that have been carried out, the system that has been built can run well. All the features created can function properly and can display weather results according to the Naïve Bayes algorithm calculations. The tool that was built can receive data and send data into the database. The application that was built can implement the Naïve Bayes algorithm and has an average accuracy of 92.49%.
Sistem Prediksi Produksi Beras Menggunakan Multiple Linear Regression untuk Optimalisasi Ketahanan Pangan di Kabupaten Minahasa Raharusun, Oliver Simon Hardianto; Hasibuan, Alfiansyah
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.376

Abstract

Ketahanan pangan merupakan isu strategis yang memerlukan pendekatan berbasis data, terutama bagi Kabupaten Minahasa yang bergantung pada sektor agraris. Prediksi produksi beras menjadi penting untuk memastikan ketersediaan pangan yang memadai di tengah tantangan lingkungan dan agraris. Penelitian ini bertujuan untuk mengembangkan model prediksi produksi beras menggunakan regresi linear berganda, dengan memanfaatkan variabel-variabel seperti luas panen, curah hujan, jumlah hari hujan, suhu rata-rata, dan kelembapan udara. Data historis digunakan untuk melatih model regresi linear berganda. Evaluasi dilakukan menggunakan metrik seperti R-squared (R²) dan Mean Absolute Percentage Error (MAPE) untuk mengukur performa prediksi. Analisis statistik digunakan untuk mengidentifikasi variabel yang paling berpengaruh terhadap produksi beras. Model menunjukkan performa yang baik dengan nilai R² sebesar 0.667, yang mengindikasikan bahwa 66,7% variabilitas data produksi beras dapat dijelaskan oleh model. Nilai MAPE sebesar 10.61% menunjukkan tingkat kesalahan prediksi yang dapat diterima. Hasil penelitian menunjukkan bahwa luas panen merupakan variabel paling signifikan dalam memengaruhi produksi, diikuti oleh curah hujan dan jumlah hari hujan. Sistem prediksi ini memiliki potensi besar untuk digunakan dalam mendukung perencanaan ketahanan pangan berbasis data. Kebijakan seperti distribusi stok, perencanaan kebutuhan lahan, dan mitigasi risiko iklim dapat dioptimalkan dengan menggunakan sistem ini. Pengembangan lebih lanjut diperlukan untuk meningkatkan akurasi prediksi dengan menambahkan variabel lain.

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