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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA IKRA-ITH Informatika : Jurnal Komputer dan Informatika JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Media Journal of General Computer Science (MJGCS) Jurnal Pengabdian Masyarakat Unama
Pareza Alam Jusia, Pareza Alam
STIKOM Dinamika Bangsa, Jambi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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WORKSHOP PENINGKATAN KUALITAS KONTEN WISATA DENGAN KECERDASAN BUATAN DI DESA WISATA MUARO PIJOAN PROVINSI JAMBI Setiawan, Roby; Arip Winanto, Eko; Gusriyanti, Dwi Ayu; Nugroho, Agus; Pahlevi, M. Riza; Alam Jusia, Pareza
Jurnal Pengabdian Masyarakat UNAMA Vol 3 No 2 (2024): JPMU Volume 3 Nomor 2 Oktober 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2024.3.2.1869

Abstract

Industri pariwisata di Desa Wisata Muaro Pijoan, Provinsi Jambi, menghadapi persaingan yang ketat di era digital, menuntut perhatian khusus pada kualitas konten wisata guna memikat minat wisatawan. Kecerdasan Buatan (AI) muncul sebagai solusi potensial untuk meningkatkan kualitas konten dan memberikan pengalaman wisata yang unggul. Data dan tren menunjukkan bahwa destinasi pariwisata yang memanfaatkan teknologi AI dapat meningkatkan daya saing dan daya tarik wisatawan. Proyek Pengabdian Kepada Masyarakat (PKM) ini difokuskan pada Desa Muaro Pijoan, dipilih karena belum sepenuhnya optimal dalam menciptakan konten yang menarik. PKM ini mendukung adaptasi desa terhadap teknologi dan digitalisasi internet dalam promosi wisata, sejalan dengan harapan pemerintah provinsi. Workshop “Peningkatan Kualitas Konten Wisata dengan Kecerdasan Buatan” dianggap sebagai langkah strategi untuk memberikan pemahaman mendalam tentang penerapan kecerdasan buatan. Tujuannya adalah meningkatkan daya saing dan daya tarik destinasi melalui pemanfaatan teknologi AI. Workshop ini juga memiliki dimensi pemberdayaan masyarakat lokal, termasuk POKDARWIS, UMKM, dan kelompok karang taruna, agar dapat memanfaatkan kecerdasan buatan sebagai alat yang dapat meningkatkan daya saing dan daya tarik destinasi pariwisata di Desa Muaro Pijoan
Perancangan E-Marketplace Wedding Organizer Berbasis Web (Studi Kasus Raja’i Decoration) Ophelia, Chandy; Reflika, Mutiara; Alam Jusia, Pareza; Nurul Marwiyah, Siska
Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Vol. 4 No. 2 (2024): Vol. 4 No. 2 Desember 2024
Publisher : LPPM ITB AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/jikobis.v4i2.117

Abstract

Raja’i Decoration merupakan salah satu dari sekian banyak penyedia layanan jasa pernikahan (Wedding Organizer) yang ada di Muaro Jambi. Berdasarkan pengamatan dan wawancara, proses pemesanan di Raja'i Decoration melibatkan konsumen yang datang langsung. Mereka memiliki opsi untuk memilih paket yang telah ditetapkan dengan harga tertentu atau membuat paket yang disesuaikan sesuai keinginan mereka sendiri. Proses ini meliputi persiapan acara pernikahan seperti menentukan jadwal, memilih gaun pengantin, lokasi acara, menyusun kartu undangan dan souvenir, serta menyediakan peralatan yang dibutuhkan selama upacara pernikahan, termasuk layanan bridal, catering, pengisi acara, MC, fotografi, dan lainnya. Tujuan penelitian ini Mengetahui dan mengidentifikasi kelemahan-kelemahan pada proses pemesanan paket wedding pada Raja’i Decoration yang sedang berjalan dan merancang E-Marketplace Wedding Organizer pada Raja’i Decoration menggunakan bahasa pemograman PHP dengan framework laravel dan database MYSQL. Penulis melakukan pengembangan sistem menggunakan metode Waterfall dan menerapkan pendekatan Unified Modeling Language (UML). Dengan adanya sistem pemesanan ini, pelayanan yang cepat dan tepat dapat diberikan kepada konsumen melalui website. Dengan sistem ini, konsumen tidak perlu lagi menghubungi pihak pemilik wedding untuk mengklarifikasi pembayaran, karena dalam sistem tersebut telah disediakan menu khusus untuk mengklarifikasi pembayaran dengan mudah dan cepat.
Perancangan Sistem Informasi Monitoring Inventaris Dan Pengadaan Aset Pada Kantor Kecamatan Paal Merah Kota Jambi Berbasis Web Febrina Mardayani, Risa; Alam Jusia, Pareza; Setiawan, Roby
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 5 No 1 (2025): JMS Vol 5 No 1 Maret 2025
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2025.5.1.2008

Abstract

The Paal Merah District Office of Jambi City is located at Jl. Sultan Syahril RT.16 Talang Bakung Village, Jambi City. In the data processing process using Microsoft Excel, but there are still many obstacles in data processing, such as the difficulty of recording Inventory Monitoring and Asset Procurement data, planning previously planned activities because the data search process is slow, data does not appear automatically so that it must be input repeatedly, and data cannot be integrated because there is no database. The purpose of this study was to analyze the current system, in order to overcome the problems faced at the Paal Merah District Office of Jambi City, by designing a Web-based Information System for Monitoring Inventory and Procurement of Assets at the Paal Merah District Office of Jambi City. The stages that will be carried out in solving the problems discussed are, identifying, searching for information based on theoretical foundations, collecting data using observation and interview methods, analyzing to find solutions to the problems faced by the Paal Merah District Office of Jambi City. The system development method uses a waterfall model, the implementation of this research uses the PHP Program Language and MySQL DBMS, to produce data processing applications that are expected to facilitate data processing and report generation.
Peningkatan Performa Naive Bayes dengan Fitur Chi-Square pada Analisis Sentimen Komentar Pengguna Aplikasi Netflix Jusia, Pareza Alam; Pahlevi, Riza; Pardamean Simanjuntak, Daniel Sintong; Jasmir
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.532

Abstract

This study discusses sentiment analysis using the Naïve Bayes algorithm with Chi-Square. The purpose of this study is to determine the effect of Chi-Square feature selection on the performance of the Naïve Bayes algorithm in analyzing document sentiment. The research data was taken from Netflix Application user comments. Testing was carried out by analyzing document sentiment with and without Chi-Square feature selection. Furthermore, it was evaluated using the accuracy, precision, and recall methods. The results of this study are that the addition of CS features to NB significantly improves all evaluation metrics, especially recall and F1-score, indicating that additional features help improve the model's ability to understand data. The combination of NB + CS with a 70:30 split gives the best results, making it the optimal choice.
Implementasi Dan Pengembangan Dashboard Akademik Feeder PDDikti Dengan Data Mart: Studi Kasus Universitas Dinamika Bangsa Rahim, Abdul; Wardani, Muhammad; Alam Jusia, Pareza; Siswanto, Agus
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 1 (2025): JAKAKOM Vol 5 No 1 APRIL 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.1.2184

Abstract

Reporting academic data to the Pangkalan Data Pendidikan Tinggi (PDDIKTI) is a mandatory requirement for higher education institutions to ensure the validity and synchronization of student, lecturer, course, and academic activity data. However, in practice, several challenges arise, such as discrepancies between the internal academic system (Siakad) and the Feeder PDDIKTI, delays in reporting, and difficulties in monitoring synchronization status in real-time. This study aims to develop an academic data reporting monitoring dashboard based on a Data Mart, enabling efficient and centralized monitoring of academic data synchronization. The dashboard integrates data from Feeder PDDIKTI and Siakad through the Extract, Transform, Load (ETL) process to generate accurate and easily interpretable information. The findings indicate that the developed dashboard effectively visualizes academic data synchronization status in real-time, reduces reporting errors, and accelerates the validation process before submission to PDDIKTI. Thus, this dashboard serves as an effective solution to support academic data governance in higher education institutions
Ekstraksi Fitur untuk Peningkatan Klasifikasi Teks Komentar Video Youtube Spam Menggunakan Deep Learning Jasmir, Jasmir; Riyadi, Willy; Jusia, Pareza Alam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5249

Abstract

The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments
Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases Jusia, Pareza Alam; Rahim, Abdul; Yani, Herti; Jasmir, Jasmir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5710

Abstract

Heart disease is a major problem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data has collaborated with information technology such as machine learning to detect, predict, and classify diseases. This research aims to improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with particle swarm optimization (PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improve the K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935.
PELATIHAN A.I PROMPTING UNTUK PENINGKATAN KEMAMPUAN BELAJAR MANDIRI PADA SISWA-SISWI SMA NEGERI 4 MUARO JAMBI Pebrianto, Feri; Beny; Yani, Herti; Rahim, Abdul; Siswanto, Agus; Alam Jusia, Pareza; Paramitha, Cindy
Jurnal Pengabdian Masyarakat UNAMA Vol 4 No 1 (2025): JPMU Volume 4 Nomor 1 April 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2025.4.1.2157

Abstract

Pelatihan kecerdasan buatan (AI) memiliki potensi besar dalam mendukung pembelajaran mandiri di kalangan siswa – siswi. Kegiatan Pengabdian Kepada Masyarakat (PKM) berupa "Pelatihan AI Prompting untuk Peningkatan Kemampuan Belajar Mandiri pada Siswa-Siswi SMA Negeri 4 Muaro Jambi." Kegiatan ini bertujuan untuk memperkenalkan aplikasi AI serta memberikan keterampilan kepada siswa dalam merancang instruksi atau "prompt" yang jelas dan efektif untuk berinteraksi dengan AI, seperti ChatGPT dan Gemini AI. Dengan pelatihan ini, diharapkan siswa mampu memanfaatkan AI sebagai alat bantu dalam proses belajar, meningkatkan kemampuan belajar mandiri, dan memperluas akses mereka terhadap informasi yang relevan. Kegiatan ini direncanakan berlangsung selama enam bulan dengan target siswa SMA Negeri 4 Muaro Jambi, serta hasil luaran berupa peningkatan pemahaman siswa tentang AI.
Comparative analysis of word embedding features to improve the performance of deep learning models on social media data Jasmir, Jasmir; Alam Jusia, Pareza; Arvita, Yulia; Gunardi, Gunardi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9200

Abstract

In this study, we apply various deep learning methods incorporating word embedding features to evaluate their impact on improving classification performance in sentiment analysis. The methods employed include conditional random field (CRF), bidirectional long short term memory (BLSTM), and convolutional neural network (CNN). Our experiments utilize social media data from restaurant review. By testing different iterations of these deep learning techniques with various word embedding features, we found that the BLSTM algorithm achieved the highest accuracy of 80.00% before integrating word embedding features. After incorporating word embeddings, the BLSTM with the word2vec feature achieved an accuracy of 87.00%. Notably, the CNN showed a significant improvement with the FastText feature. Considering all evaluation metrics—accuracy, precision, recall, and F1-score—the BLSTM algorithm consistently demonstrated the best performance across different word embeddings.
Co-Authors ., Amroni Abdul Rahim Agus Nugroho, Agus Agus Siswanto Ali Sadikin Amroni Arvita, Yulia Aulia, M. Farhan Beny Dodi Sandra Errissya Rasywir Febrina Mardayani, Risa Gunardi Gunardi Gusriyanti, Dwi Ayu Halim, Rina Hartiwi, Yessi Hendrawan Hendrawan Herti Yani, Herti Irfan, Fadhel Muhammad ISW, Ibnu Sani Wijaya Jasmir Jasmir, Jasmir Juliana Juliana Kurniabudi, Kurniabudi Mery NOVIANTO, YUDI Nurul Marwiyah, Siska OPHELIA, CHANDY Pahlevi, M. Riza Paramitha, Cindy Pardamean Simanjuntak, Daniel Sintong Pebrianto, Feri Putra, Rafi Septiandi Reflika, Mutiara Riza Pahlevi, Riza ROBY SETIAWAN Rofi’i, Imam SAYID ACHMAD MAULANA Sharipuddin Sharipuddin, Sharipuddin Stefani, Dina Maria Tesaloniki, Viola Wardani, Muhammad WIJAYA, IBNU SANI WILLY RIYADI Yovi Pratama