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All Journal Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Informasi Interaktif Jurnal Tekinkom (Teknik Informasi dan Komputer) G-Tech : Jurnal Teknologi Terapan Journal of Information Technology (JIfoTech) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Info Sains : Informatika dan Sains Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Instink: Inovasi Pendidikan, Teknologi Informasi dan Komputer Infact: Jurnal Sains dan Komputer Journal of Computing Innovations and Emerging Technologies
Noviyanti. P
Institut Shanti Bhuana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Social Sciences Other

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IMPLEMENTASI ALGORITMA DEPTH-FIRST SEARCH DAN BREADTH-FIRST SEARCH PADA DOKUMEN AKREDITASI Yuliana Yuliana; Noviyanti Noviyanti; Mudawil Qulub
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 7, No 1 (2024): February 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v7i1.1733

Abstract

Sistem arsip dokumen dapat digunakan sebagai media penyimpanan data untuk memudahkan persiapan proses asesmen lapangan untuk akreditasi. Mekanisme pencarian data merupakan bagian penting dalam arsip digital. Dengan menggunakan Teknologi Kecerdasan Buatan dalam teknik pencarian yaitu depth-first search dan breadth-first search. Kedua metode ini dipadukan untuk menyelesaikan permasalahan yang tentunya mempunyai kelebihan dan kekurangan. Sistem dokumen digital dapat melakukan proses pencarian, penyampaian, pemantauan dan pengambilan data. Dalam proses pengujian dan mekanisme analisa pencarian data, sistem menerapkan output teknik penggabungan/kolaborasi dari depth-first search dan breadth-first search yang diakhiri melalui penemuan mendalam ke dalam database untuk menyesuaikan parameter hingga ditentukan query untuk mengeksekusi hasil keluaran parameter dan kemudian umpan balik diberikan kembali ke sistem. Topik penelitiannya ini menemukan jalur cepat akreditasi penyimpanan dokumen arsip dengan teknik penggabungan algoritma yang bisa digunakan dalam menemukan rute cepat pada saat menemukan tujuan tertentu. Rekomendasi pada penelitian yang ingin mendalami topik yang sama adalah dengan menggabungkan tambahan algoritma lain, yaitu teknik blind search pada Artificial Intelligence.
Analisis Sentimen Tweet untuk Mendeteksi Keinginan Bunuh Diri menggunakan Pendekatan Machine Learning pada Data Besar Noviyanti. P; Candra Gudiato; Listra Frigia Missianes Horhoruw
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6154

Abstract

Suicidal ideation is a serious mental health problem and is often difficult to detect in its early stages. Social media, especially Twitter, is one of the platforms widely used by individuals to express their feelings and emotional conditions, including expressions of suicidal ideation. This study aims to develop a machine learning model that can analyze the sentiment of tweets related to suicidal ideation using big data. The data used in this study consisted of tweets that had been processed for sentiment analysis, which were then classified into three sentiment categories, namely positive, negative, and neutral. The machine learning model applied was Naive Bayes. The results of the model evaluation showed that this model had an accuracy of 72%, with precision and recall values varying depending on the sentiment category. The highest precision was recorded in the negative and neutral categories (0.91), while the highest recall was recorded in the positive category (0.97). This study provides insight into the potential use of machine learning-based sentiment analysis to detect signs of suicidal ideation through big data from social media that can help in early detection of mental health problems.
Decision Support System for Village Head Election Using the Weighted Product Method: Case Study in Lumar Village Noviyanti; Angelia Deli; Laura Gloria
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.1

Abstract

The election of the village head is an important process in determining leaders who can manage the village government effectively and meet the needs of the community. Election leader of village is very important to determine the direction of the region and the importance of the capabilities of the chosen leader based on real data. This study discusses the application of the Weighted Product (WP) method in the decision-making support system for the election of village heads in Lumar Village. The WP method is used because it is able to handle various criteria by giving weight to each criterion according to its level of importance. The criteria used include work experience, education, integrity, and community support. This system is designed to process data in a structured and transparent manner, generating a preference value for each prospective village head. The candidate with the highest score is considered the most qualified. The results of the study show that the WP method improves the accuracy, objectivity, and efficiency of the village head election process, resulting in accountable decisions.
Implementation of IoT-Based Automatic Irrigation System Using Decision Tree Algorithm on Hydroponic Garden at Institut Shanti Bhuana Bengkayang Kristian Novando; Noviyanti P
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.6

Abstract

This study presents the development and implementation of an automatic irrigation system based on the Internet of Things (IoT) utilizing the Decision Tree algorithm. The system was applied in a hydroponic garden at Institut Shanti Bhuana Bengkayang. It employs a water level sensor to detect the volume of water, which is then processed using the Decision Tree classification to determine whether the irrigation valve should be opened or closed. Data collected from the sensor were analyzed both manually and programmatically to find the optimal threshold for decision-making. The system was integrated with the Blynk platform, allowing real-time monitoring and control. Testing was conducted over 7 days with 210 data points, and the classification model achieved an accuracy of 100%. The results indicate that the proposed system effectively automates irrigation, minimizes manual intervention, and provides a reliable solution for small-scale smart farming applications.
Predicting the Potential of Renewable Solar Energy Based on Weather Data in Indonesia Using the Random Forest Method Noviyanti. P; Maya Sari; Kusnanto Kusnanto
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7776

Abstract

Renewable energy plays a crucial role in reducing greenhouse gas (GHG) emissions. Excessive use of fossil fuels, such as coal, can produce GHG emissions that trigger extreme weather and global warming. Therefore, efforts to increase renewable energy utilization are necessary, in line with the Government Work Plan (RKP) target, which targets renewable energy contributions to reach 23% by 2025. This study aims to predict the potential for solar renewable energy in an area based on radiation, temperature, and rainfall variables. The method used is a supervised learning-based Random Forest. Weather data was obtained through the Open Meteo API, then processed by assigning weights to variables to produce output labels, which were then used in the classification process and model performance evaluation. The results showed that the Random Forest model produced an accuracy of 99.82%, with predictions of low/no potential energy being completely correct, medium energy potential experiencing only one error, and high energy potential also experiencing only one error. Based on these findings, the Random Forest method has proven effective in predicting solar power potential with high accuracy and is able to identify variables with the highest to lowest levels of importance.
Co-Authors Angelia Deli Angga Domeos Manggara Benecta Ina Purwanti Cahyaningtyas, Christian Fra Siskus Dian Arianto Fra Siskus Dian Arianto Gudiato, Candra Jones Parlindungan Nadapdap Kristian Novando Kusnanto Kusnanto Laura Gloria Listra Frigia Missianes Horhoruw Listra Frigia Missianes Horhoruw Mansuestus Fransiskus Maya Sari Mira Mira Mira Mudawil Qulub Oskar Paulus Alfianus Santi Thomas santi thomas Usman Usman Veneranda Rini Hapsari Vuspitasari, Benedhikta Kikky Yoseph Erbito Yuliana Yuliana Yuliana Yuliana Yuliana Yuliana Yuliana