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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Informatika dan Teknik Elektro Terapan POSITIF Sistemasi: Jurnal Sistem Informasi Tech-E RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Tekno Kompak Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology JURNAL ILMIAH GLOBAL EDUCATION Algoritme Jurnal Mahasiswa Teknik Informatika TECHSI - Jurnal Teknik Informatika Journal of Computer Engineering, Electronics and Information Technology Journal of Advanced Computer Knowledge and Algorithms Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rizal S.Si., M.IT, Rizal
Teknik Informatika Universitas Malikussaleh
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Peningkatan Efisiensi Kecepatan dan Akurasi Rekapitulasi Faktur Pajak Dengan Optical Character Recognition Di Orbit Future Academy Lidya Rosnita; Rizal Tjut Adek
TECHSI - Jurnal Teknik Informatika Vol. 14 No. 2 (2023)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v14i2.14861

Abstract

Kemajuan ilmu pengetahuan terutama dalam ranah Artificial Intelligence (AI), telah membawa perubahan signifikan bagi kehidupan manusia. Di Indonesia, Orbit Future Academy (OFA) hadir sebagai lembaga pelatihan terbesar dalam bidang AI. Program AI 4 Jobs bertujuan untuk mempersiapkan individu dalam memasuki dunia kerja yang didominasi oleh teknologi AI. Program didesain untuk mengenalkan teknologi AI kepada pelajar guna menginspirasi pengembangan produk AI yang berdampak sosial. Berdasarkan pengetahuan tentang kemampuan AI, penulis menemukan suatu tantangan dalam kantor konsultan pajak yaitu pengolahan dokumen faktur pajak yang masih dilakukan secara manual, dimana hal tersebut dapat diatasi dengan kehadiran AI yang mampu mengolah data berulang dengan efisiensi tinggi. Untuk menyelesaikan tugas tersebut, sebuah website AI dibangun dengan memanfaatkan domain AI Computer Vision dan menggunakan model Optical Character Recognition (OCR) dengan library deep learning EasyOCR, Pytesseract, dan PDF Plumber. Tahapan pada pembuatan AI ini terdiri dari problem scoping, data acquisition, data exploration, modeling, evaluation, dan deployment. Pengujian dilakukan menggunakan dua jenis file faktur pajak (PDF dan JPG) yang masing-masing terdiri dari lima sampel faktur pajak, diujikan langsung pada website dengan tiga library yang berbeda. Hasil pengujian menunjukkan tingkat accuracy, recall, precision, dan f-score deteksi faktur pajak sebesar 100%. Pengujian PDF Faktur Pajak dengan PDF Plumber memiliki tingkat accuracy, recall, precision, dan f-score sebesar 100%. Pengujian gambar faktur pajak dengan Tesseract OCR memiliki tingkat accuracy sebesar 60%, recall 100%, precision 60% dan f-score 75%. Pengujian gambar faktur pajak dengan EasyOCR memiliki accuracy, recall, precision, dan f-score 100%.
Web-Based Geographic Decision Support System for Boarding House Recommendation Using SMART and A* Muslimatul Magfirah; Rizal Rizal; Safwandi Safwandi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13361

Abstract

Selecting a boarding house is an important need for incoming students, especially in urban areas that offer many housing alternatives. The large number of available options often creates difficulties in choosing accommodation that matches user preferences. This study aims to develop a WebGIS-based boarding house recommendation system in Lhokseumawe by integrating the SMART method with the A* algorithm, where the travel distance generated by the A* algorithm is incorporated directly into the SMART evaluation process. The SMART method was used to evaluate and rank boarding house alternatives based on the criteria of price, distance, room size, bathroom type, Wi-Fi, air conditioning, and bed availability, while the A* algorithm was employed to determine the shortest travel route. The system was evaluated through calculation validation by comparing the SMART scores generated by the system with manual calculations, as well as through a preliminary user evaluation. The results showed that the system produced recommendation scores consistent with manual calculations, and the user evaluation indicated that the recommended boarding houses matched user preferences in the evaluated scenarios.
Analisis Komparatif Deteksi Spammer Menggunakan Kombinasi Mean Shift Clustering dan Local Mean K-Nearest Neighbor pada Lalu Lintas Jaringan Komputer OK Muhammad Majid Maulana; Hafizh Al Kautsar Aidilof; Nurdin Nurdin; Muhammad Sayuti; Rizal Tjut Adek; Zara Yunizar
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10005

Abstract

Alamat IP publik pada jaringan komputer berskala besar sering kali menghadapi risiko pemblokiran oleh layanan server global akibat aktivitas lalu lintas mencurigakan yang dipicu oleh perangkat spammer. Penelitian terdahulu telah mengusulkan sistem deteksi menggunakan kombinasi K-Medoids Clustering dan Gaussian Naïve Bayes Classifier. Namun, pendekatan statistik global tersebut masih menyisakan kelemahan berupa angka deteksi melolos (False Negative) yang signifikan akibat karakteristik ketimpangan kelas (class imbalance) yang ekstrem pada lalu lintas jaringan riil. Untuk mengatasi batasan tersebut, penelitian ini menerapkan sebuah pendekatan hybrid baru dengan mengintegrasikan algoritma Mean Shift Clustering dan Local Mean K-Nearest Neighbor (LMKNN). Data lalu lintas diekstraksi dari gateway Mikrotik RouterBoard RB850Gx2 pada jaringan Wi-Fi e-UnimalNet Universitas Malikussaleh. Algoritma Mean Shift yang berbasis densitas berhasil mengisolasi karakteristik lalu lintas secara mandiri menjadi 19 klaster spasial tanpa memerlukan inisialisasi jumlah kelompok di awal. Klaster-klaster mikro kemudian dilebur menggunakan ambang batas densitas menjadi representasi binary class (Normal dan Spammer). Selanjutnya, algoritma LMKNN diterapkan untuk mengklasifikasikan data uji dengan memanfaatkan metrik rata-rata jarak lokal guna memberikan representasi yang adil bagi kelas minoritas (spammer). Hasil eksperimen menunjukkan performa klasifikasi yang luar biasa, di mana nilai hiperparameter tetangga lokal optimal pada K=3 berhasil mencatatkan nilai Akurasi, Presisi, Recall, dan F1-Score mutlak sebesar 100%. Pendekatan ini terbukti sukses memangkas angka False Negative dari 23 data pada model konvensional sebelumnya menjadi 0 data murni, menjadikannya solusi deteksi anomali yang sangat andal dan robust untuk administrator jaringan.
PENGEMBANGAN AI DALAM PENYAJIAN INFORMASI BERITA WILAYAH LHOKSEUMAWE MENGGUNAKAN NER DAN WEB SCRAPING Rajaul Bani Safar; Rizal Tjut Adek; Nunsina Nunsina
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10766

Abstract

Kebutuhan informasi lokal di Kota Lhokseumawe belum terpenuhi secara optimal karena mesin pencari berita arus utama masih bersifat umum dan minim filter spasial tingkat kota. Penelitian ini bertujuan merancang sistem pencarian dan peringkasan berita lokal berbasis Artificial Intelligence (AI) untuk mengatasi masalah information overload. Metode yang diusulkan mengintegrasikan web scraping (HTTPX dan Selenium) untuk ekstraksi data dinamis, Named Entity Recognition (NER) untuk filter entitas lokasi geografis, serta extractive summarization dan semantic similarity matching menggunakan model Sentence Transformer. Hasil pengujian kuantitatif terhadap metode NER menunjukkan performa yang sangat baik dalam menyaring lokasi dengan nilai Precision 80,49%, Recall 91,67%, dan F1-Score 85,72%. Secara kualitatif, ringkasan yang dihasilkan mampu menyajikan informasi esensial secara relevan, mudah dibaca, dan koheren, dengan format keluaran yang adaptif (naratif atau daftar rekomendasi) menyesuaikan kueri pengguna. Kesimpulannya, integrasi metode pemrosesan bahasa alami ini terbukti efektif menyaring dan meringkas konten berita lokal secara spesifik. Pengembangan selanjutnya dapat mengeksplorasi metode peringkasan abstraktif.
Gold Price Prediction Using Long-Short Term Memory Algorithm Based on Web Application Rodiatul Adawiyah Dalimunthe; Rizal Tjut Adek; Cut Agusniar
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.724

Abstract

Gold is a significant investment asset, particularly in times of economic instability. Various factors, including decisions by financial authorities, inflation, and global economic dynamics, influence the fluctuations in gold prices. Accurately predicting gold prices is valuable for investors when making investment decisions. This study aims to utilize the Long Short-Term Memory (LSTM) algorithm for predicting gold prices and develop a web-based application connected to Yahoo Finance to acquire real-time gold price data. The LSTM algorithm was chosen because it handles time series data with long-term dependencies. LSTM has an architecture that allows the model to retain relevant information over long periods and forget irrelevant data. In this study, the developed LSTM model produced a Mean Absolute Error (MAE) of 19.81, indicating that the average prediction deviates by approximately 19.81 units from the actual value. Furthermore, an average Mean Absolute Percentage Error (MAPE) of 0.83% demonstrates the high prediction accuracy. The results of this study show that LSTM is an effective method for predicting gold prices. The resulting web application allows users to access gold price projections interactively, thereby assisting investors in making more accurate and data-driven decisions with easy access. Additionally, the web application offers customizable features such as adjusting prediction parameters and visualizing results in real time.  These features not only enhance user engagement but also improve decision-making processes. This research provides a practical tool for optimizing investment strategies in a dynamic economic environment by leveraging machine learning and seamless web integration.
Online Newspaper Clustering in Aceh using the Agglomerative Hierarchical Clustering Method Rizal Tjut Adek; Rozzy Kesuma Dinata; Ananda Ditha
International Journal of Engineering, Science and Information Technology Vol 2, No 1 (2022)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.616 KB) | DOI: 10.52088/ijesty.v2i1.206

Abstract

The rapid progress in the field of information technology, especially the internet, has given birth to a lot of information. The ease of publishing an article on a website causes an explosion of news pages which will certainly confuse readers. The diversity and the increasing number of news articles make it increasingly difficult for internet users to find news and large piles of news data on online newspaper sites in Aceh. The grouping of text documents is needed to classify news in online newspapers in Aceh based on the content contained in news articles. In this study, the process of grouping online news in Aceh was tried using the Agglomerative Hierarchical Clustering method. News is grouped with a Bottom-Up design strategy that starts with placing each object as a cluster then combined into a larger cluster based on the similarity of keywords in each news, then the cluster results are compared and put into each news category. The research design was carried out in a structured manner using data flow diagrams in forming the research framework. The study was conducted by taking online news text data on 10 online news websites in Aceh from July 2016 to March 2017 with 1000 randomly generated documents. The process of crawling news data is done using a php script which will only take text files from the news on the website. News grouping is done based on religion, politics, law, sports, tourism, education, culture, economy and technology. The results of the grouping performance of the Agglomerative Hierarchical Clustering method in this study have an average accuracy of 89.84%.
Co-Authors Aidilof, Hafizh Al Kautsar Aisah, Sri Purwani Ali Imran Nasution Ananda Ditha Annas, Muhammad Annisa Helmina Bustami Bustami Cut Agusniar Dea Azzahra Dyah Ika Rinawati Fachruzi, Faza Reihan Fadlisyah Fadlisyah Fasdarsyah Fasdarsyah fauzan Fauzan Hafizh Al Kautsar Aidilof Hafizh Al Kautsar Aidilof Hafizh Al Kautsar Aidilof Helmi Naluri ilham - sahputra Khairat Nur Lidya Rosnita M Ishlah Buana Angkasa M Rizwan M Suhendri M. Fauzan Mahendra Febriliansyah Maizuar Maizuar Muhammad Fauzan Muhammad Fikry Muhammad Muhammad Muhammad Sayuti Mukhlis Mukhlis Mulaesyi, Syibbran Munirul Ula Muslimatul Magfirah Muthmainnah Muthmainnah Novia Hasdina Novia Hasdyna Nunsina Nunsina Nura Usrina Nurdin Nurdin OK Muhammad Majid Maulana Rahmat Rinaldi Rajaul Bani Safar Risawandi, Risawandi Rizki Suwanda Rodiatul Adawiyah Dalimunthe Rozzy Kesuma Dinata Safwandi Safwandi Said Fadlan Anshari Siregar, Siti Chairani Siti Nadilla Subhan Hartanto Taufiqurrahman Taufiqurrahman Umar Khalil Yesy Afrilia Yesy Afrillia Zahratul Fitri, Zahratul Zainul Anwar Adi Putra Zalfie Ardian Zara Yunizar Zara Yunizar zulfhazli zulfhazli