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All Journal PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Pareza Alam Jusia
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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LITERASI DAN PELATIHAN MANAGEMENT CLOUD COMPUTING BAGI GURU-GURU DALAM MENYIMPAN DATA SEKOLAH BERBASIS DIGITAL DI SMK NEGERI 5 MUARO JAMBI: Pelatihan Penyimapan Google Drive Willy Riyadi; Ibnu Sani Wijaya ISW; Jasmir; Pareza Alam Jusia; Amroni; Khairuldi
Jurnal Pengabdian Masyarakat UNAMA Vol 2 No 1 (2023): JPMU Volume 2 Nomor 1 April 2023
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2023.2.1.729

Abstract

In the current world of information technology, digital data is very important and efficient for all activities carried out by us in the work we do, including teaching staff such as teachers, lecturers and others. Many free platforms are provided by several companies that provide online storage (cloud computing) such as Google Drive, Dropbox, One Drive, and others. However, based on interviews with the school principal and observations made at SMK Negeri 5 Muaro Jambi, they have not utilized this online storage. Whereas those in teacher activities when reporting data such as grades and others must be submitted digitally and also stored in online storage. But the teachers at the school have not been able to apply these rules because they do not have the ability or expertise to operate digital data that is stored online (cloud computing). From these problems, teachers at SMK Negeri 5 Muaro Jambi need to improve their ability to process digital-based data stored online, in this case using Google Drive. The activity planning was carried out in the SMK Negeri 5 Muaro Jambi laboratory with the participants being teachers at the school. The output target of this activity is the publication of a journal in the Community Service Journal, Dinamika Bangsa University Jambi.
Pelatihan Digital Marketing menggunakan Facebook Ads dan Marketplace Shopee sebagai strategi peningkatan Penjualan Pada UMKM Madu Mayeesha nurhadi; Pareza Alam Jusia; Ronald Naibaho; Khairuldi; Eko Arip Winanto; Dodi Sandra; Beni Irawan; Suwanto
Jurnal Pengabdian Masyarakat UNAMA Vol 2 No 1 (2023): JPMU Volume 2 Nomor 1 April 2023
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2023.2.1.745

Abstract

The community service activities carried out are community service activities funded by the Jambi Dinamika Bangsa Foundation. The implementation of this activity is carried out in the form of practicum and discussion to train training participants, namely Madu Mayeesha UMKM actors in terms of graphic design and Facebook Ads and Shopee Marketplace, to increase sales through digital marketing. This training uses the following methods: problem formulation stage, solution determination stage, settlement method, evaluation stage, and output. This training utilizes Graphic Design software such as: CorelDraw, Photoshop and Canva to create promotional designs or advertising materials for mayeesha honey products
Analisis Sentimen Terhadap Tagar Kabur Aja Dulu Di Twitter Menggunakan Metode Lexicon-Based Eko Arip Winanto; ali, zidan; Pareza Alam Jusia; Sharipuddin
Jurnal PROCESSOR Vol 20 No 2 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.2.2542

Abstract

Tagar #KaburAjaDulu sempat menjadi perbincangan hangat di media sosial Twitter, mencerminkan respons masyarakat digital Indonesia terhadap dinamika sosial dan politik yang sedang berlangsung. Penelitian ini bertujuan untuk mengevaluasi sentimen publik terhadap tagar tersebut dengan menerapkan pendekatan lexicon-based menggunakan InSet (Indonesia Sentiment Lexicon).Data penelitian diperoleh melalui teknik scraping dengan pustaka Tweet Harvest, menghasilkan 581 tweet berbahasa Indonesia yang memuat tagar #KaburAjaDulu. Analisis dilakukan menggunakan Google Colaboratory dengan dukungan pustaka Python. Tahapan penelitian mencakup pra-pemrosesan teks (pembersihan data, tokenisasi, stopword removal, serta stemming/lematisasi), klasifikasi sentimen dengan metode lexicon-based, dan visualisasi hasil.Hasil analisis menunjukkan bahwa sentimen negatif mendominasi dengan persentase 41,72%, diikuti sentimen netral sebesar 33,73% dan sentimen positif sebesar 24,55%. Kata-kata dominan pada kategori negatif merepresentasikan kritik, keluhan, dan sindiran yang banyak disampaikan dalam gaya bahasa satir khas media sosial. Temuan ini mengindikasikan bahwa tagar #KaburAjaDulu lebih sering digunakan sebagai sarana ekspresi ketidakpuasan publik terhadap kondisi sosial-politik nasional.Secara keseluruhan, pendekatan lexicon-based terbukti efektif dalam memberikan gambaran umum mengenai kecenderungan opini publik tanpa memerlukan pelatihan model. Namun, metode ini memiliki keterbatasan dalam menangkap makna kontekstual dari bahasa informal maupun sarkastik. Oleh karena itu, penelitian ini dapat dijadikan pijakan awal bagi studi lanjutan yang mengintegrasikan pendekatan machine learning untuk meningkatkan akurasi analisis sentimen pada media sosial.
Prediksi Mahasiswa Berpotensi Non-Aktif Menggunakan Algoritma Decision Tree Classifier Rahim, Abdul; Pareza Alam Jusia
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3692

Abstract

Dengan pertumbuhan jumlah mahasiswa yang semakin dinamis, kebutuhan untuk menerapkan strategi preventif guna meningkatkan tingkat retensi mahasiswa menjadi semakin penting. Penelitian ini bertujuan untuk mengembangkan model yang dapat digunakan untuk mendeteksi mahasiswa yang berpotensi status akademiknya menjadi non-aktif menggunakan algoritma Decision Tree Classifier di lingkungan Universitas Dinamika Bangsa. Data yang digunakan dalam penelitian ini mencakup beragam variabel seperti data pribadi mahasiswa, nilai akademik dan informasi demografis lainnya. Proses pemodelan menggunakan Decision Tree Classifier dilakukan dengan memanfaatkan data historis mahasiswa untuk melatih model dalam mengklasifikasikan mahasiswa yang berpotensi non-aktif. Selanjutnya, model ini diuji coba pada data mahasiswa baru untuk menguji tingkat akurasi dan efektivitasnya. Hasil penelitian ini menunjukkan bahwa algoritma Decision Tree Classifier mampu memberikan kontribusi yang signifikan dalam prediksi mahasiswa yang berpotensi non-aktif dengan tingkat akurasi 95.63% dengan variabel yang paling berpengaruh adalah indeks prestasi semester 3, indeks prestasi semester 2 dan umur saat diterima.
Optimalisasi dan Perancangan Sistem Informasi Layanan Pengaduan Masyarakat di Kabupaten Tanjung Jabung Barat: Studi Kasus : Layanan Call Center “HALO USTAD” Fadillah Rahman; Pareza Alam Jusia; Masgo Masgo
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.80

Abstract

Public complaint services are an essential part of public service delivery in supporting the government’s rapid response to various social issues and emergency situations. In West Tanjung Jabung Regency, public complaint services are provided through the HALO USTAD 112 Call Center managed by the Department of Communication and Informatics. However, the existing service still faces several limitations, including the lack of optimal integration in complaint data management, inadequate documentation of reports based on regional classifications, and limited capabilities in storing and retrieving complaint data. This study aims to optimize the HALO USTAD 112 Call Center service through the design of a mobile-based public complaint information system, so that the processes of receiving, managing, and monitoring reports can be carried out more effectively and in a structured manner. The system development applies the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. The designed information system includes key features such as user and admin login, complaint submission, report management and verification, report monitoring, statistical visualization of complaint data, and regional-based report recapitulation. The application is developed using the Flutter framework with the Dart programming language, while Supabase is utilized as the backend integrated with a PostgreSQL database. The results of this study are in the form of a system design and prototype that are expected to improve the quality of public complaint services and support more accurate, integrated, and efficient data management.
Penerapan Metode K-Means Clustering Untuk Menentukan Faktor Resiko Pada Penderita Diabetes Melitus Melda Septriani; Pareza Alam Jusia; Rudolf Sinaga; Shinta Renova Putri; Firyal Najla 'Afifah
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.94

Abstract

Diabetes Mellitus is a disease caused by the failure of the pancreas organ in producing the hormone insulin in excess causing increased blood sugar levels and resulting in a lack of insulin. This study discusses the application of the k-means clustering method to determine risk factors for diabetes mellitus. By using the clustering method, data will be grouped into several clusters or groups which in this study compare by applying several data mining tools such as RapidMiner, SPSS, WEKA, and Python. From the results of the comparison carried out resulted in 5 calculations, namely the manual calculation of cluster 1 with a ratio value of 73% being the first priority, calculations using RapidMiner resulting in cluster 3 with a ratio value of 58% being the first priority, calculations using SPSS cluster 2 with a ratio value of 34% being the first priority, and calculations using Python produce cluster 1 with a ratio value of 55% being the first priority.
Sentimen Analisis Review Aplikasi Cek Bansos Pada Google Play Store Menggunakan Metode Naïve Bayes Ary Ardiansyah; Pareza Alam Jusia; Rudolf Sinaga; Clarisa Putri Valentina; Pardede, Nadia
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.100

Abstract

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.
Analisis dan Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Melitus M Daffa Adrian; Pareza Alam Jusia; Rudolf Sinaga; Azzahra Raihana Adriansyah; Mutammimah Mutammimah
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.114

Abstract

Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.
Analisis Pengaruh Kualitas Layanan terhadap Kepuasan Pengguna pada Website Dinas Kependudukan dan Pencatatan Sipil Kota Jambi Menggunakan Metode Webqual 4.0 Devi Saputra; Pareza Alam Jusia; Rudolf Sinaga; Syaqilla Dinata; Euis Oktapiani
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.115

Abstract

Website Accessible Jambi City Population and Civil Registry Service https://disdukcapil.jambikota.go.id. The problem with the Jambi City Population and Civil Registry Service website is that not all information is available, especially on the Profile menu there is a Media Information sub-menu, Data menu and Facilities and Infrastructure menu. On the Information Media sub-menu, there is a Demographic Data sub-menu, where the contents of the sub-menu are still empty, preventing users from obtaining information. On the Public Facilities and Infrastructure menu from the sub menu, the data cannot be accessed so that it makes users unable to get information. And in the appearance of the Jambi City Population and Civil Registry Service, when accessed via Google Chrome, the appearance is disorganized, so users have to open the website using a laptop/PC to get a website display that is orderly and easy for users to understand. Quality measurement is carried out based on user satisfaction point of view in order to improve the quality of service to the community and make optimal use of the website. In analyzing user satisfactionwebsite DUKCAPIL Jambi using the webqual 4.0 method, there are 4 variables, namely usability (usability), information quality (information quality), interaction quality (interaction quality), and user satisfaction (user satisfaction) and using the software (software) SPSS. Of the 3 hypotheses proposed, all hypotheses were accepted in this study.
Co-Authors Abdul Harris Abdul Rahim Abdul Rahim Afrizal Nehemia Toscany Agus Nugroho ali, zidan Amroni Amroni . Amroni Amroni Ary Ardiansyah Asri Suryo Widi Azzahra Raihana Adriansyah Beni Irawan Beny Beny Chindra Saputra Clarisa Putri Valentina Devi Saputra Dodi Sandra Eko Arip Winanto Euis Oktapiani Fachruddin Fachruddin Fachruddin Fachruddin Fadillah Rahman Firyal Najla 'Afifah Ibnu Hisyam Ibnu Sani Wijaya Ibnu Sani Wijaya ISW Imam Rofi’i Imam Rofi’i Jasmir Jayawarsa, A.A. Ketut Khairuldi M Daffa Adrian M Irwan Bustami MARIA BINTANG Masgo Masgo Melda Septriani Muhammad Hidayat Mutammimah Mutammimah NAIBAHO, RONALD Nurhadi Nurhadi Pardede, Nadia Prima Eka Syam putri Rina Halim Rudolf Sinaga Sharipuddin Shinta Renova Putri Suwanto Syaqilla Dinata WILLY RIYADI Yeni ardandi ZULFI KARMAN