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All Journal Jurnal Buana Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Sistem Informasi Kaputama (JSIK) Building of Informatics, Technology and Science JURNAL MAHAJANA INFORMASI JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) BEES: Bulletin of Electrical and Electronics Engineering Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Bulletin of Informatics and Data Science
Juanda Hakim Lubis
Universitas Medan Area
Aerospace Engineering Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics

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Sistem Pendukung Keputusan Pemilihan Laptop dengan Menerapkan Metode Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) Sussolaikah, Kelik; Lubis, Juanda Hakim; Sallaby, Achmad Fikri; Yuliani, Ega; Mesran, Mesran
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6525

Abstract

The lack of knowledge and information regarding laptop specifications makes ordinary people who want to buy a laptop feel confused about determining which laptop suits their needs. As a tool that can be used to select a laptop, a decision support system is needed. In decision support systems there are several methods, one of which can be used is the MOORA method (Multi-Objective Optimization on the basis of Ratio Analysis). In this research, the author will raise a case to find the best alternative from predetermined criteria to determine comparisons by rating existing alternatives using the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis)method. Based on the relative performance scores, Asus Rog GL552JX (A9) obtained the highest score of 0.217 and ranked first. This was followed by Asus A455LD (A4) with a score of 0.21585 and second place, and Acer Aspire E5-551 (A2) with a score of 0.19785 and third place. Acer One 10 S100X (A10) received the lowest score of 0.1042 and ranked last. Thus, Asus Rog GL552JX (A9) can be considered as the best laptop based on the established criteria in this study.
Decision Support System for Selecting the Best Head of Study Program Applying the Multi-Objective Optimization Method on the Basis of Simple Ratio Analysis (MOOSRA) Lubis, Juanda Hakim; Sanwani, Sanwani; Lubis, Akhyar; Mesran, Mesran; Julaysa, Julaysa; Hutapea, Novita Sari
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.105

Abstract

The Head of Study Program plays an important role as the highest leader in the structure of a study program. The Head of Study Program is responsible for the smooth running of academic activities in the study program he leads. As a key element in higher education, the Head of Study Program must lead the managerial function by planning, implementing, and controlling the academic process and managing other supporting activities. The head of the study program who shows high performance, dedication, and integrity deserves an award as the best head of the study program. This assessment aims to ensure that the Head of Study Program is able to carry out his duties properly in accordance with the rules and demands, and advance the study program in accordance with its vision and mission. Therefore, a decision support system is needed as a solution to overcome this problem, by utilizing the MOOSRA method. MOOSRA begins by formulating a decision matrix consisting of alternatives, criteria or attributes, individual weights or significance coefficients of each criterion, and performance measurements of related alternatives. Normalization is then carried out to change the attribute values into the range 0–1. The assessment results show that the Head of Study Program with the highest ranking is alternative A7, with a value of 0.896358
Performance Optimization of ERD Designs Using Cost-Based Optimization for Large-Scale Query Processing Lubis, Juanda Hakim; Handayani, Sri; Mawengkang, Herman; Napitupulu, Fajrul Malik Aminullah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4523

Abstract

The rapid growth of stored data, particularly on magnetic disks, is doubling annually for each department within a company, creating a pressing need for efficient database management. While database design is a fundamental step in establishing a high-performance system, it alone is insufficient to ensure optimal efficiency. Query optimization plays a critical role in improving data transaction speed, reducing query execution time, and enhancing overall system responsiveness. This study evaluates various relational database models under different data volumes to analyze their impact on query performance. Using the Cost-Based Optimizer method and access time measurements, we assess query costs and determine the factors influencing performance. The results indicate that among the three database models analyzed, ERD-3 consistently delivers superior performance, especially in handling complex queries. This is attributed to its modular structure, strategic indexing, and reduced full table scans, which collectively minimize query execution costs. Additionally, several key factors significantly affect query performance, including record count, attribute size, query complexity, primary and unique key usage, indexing strategies, order-by clauses, index sequences, and SQL function application. This research contributes to the field of database optimization by demonstrating that ERD structuring and cost-based query analysis significantly improve system efficiency in large-scale environments. These findings emphasize the necessity of a well-structured, scalable database model and efficient query processing techniques to accommodate large-scale data growth. The study’s conclusions provide a foundation for advanced optimization strategies, ensuring that modern database systems remain efficient and adaptable to evolving data demands.
Analisis Sentimen pada Data Saran Mahasiswa Terhadap Kinerja Departemen di Perguruan Tinggi Menggunakan Convolutional Neural Network Yuliska, Yuliska; Qudsi, Dini Hidayatul; Lubis, Juanda Hakim; Syaliman, Khairul Umum; Najwa, Nina Fadilah
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021854842

Abstract

Review atau saran dari customer dapat menjadi sangat penting bagi penyedia layanan, begitu pula saran dari mahasiswa mengenai layanan sebuah unit kerja di perguruan tinggi. Review menjadi penting karena dapat menjadi indikator kinerja penyedia layanan. Pengolahan review juga sangat penting karena dapat menjadi referensi untuk pengambilan keputusan dan peningkatan layanan yang lebih baik ke depannya. Penelitian ini menerapkan analisis sentimen pada data saran atau review mahasiswa terhadap kinerja unit kerja atau departemen di perguruan tinggi, yaitu Politeknik Caltex Riau. Analisis sentimen dilakukan dengan menggunakan Convolutional Neural Network (CNN) dan word embedding Word2vec sebagai representasi kata. CNN merupakan metode yang memiliki performa yang baik dalam mengklasifikasi teks, yaitu dengan teknik convolutional yang menggabungkan beberapa window kata pada kalimat dan mengambil window yang paling representative. Word2Vec digunakan sebagai representasi data saran dan inputan awal pada CNN, dimana Word2Vec merupakan dense vectors yang dapat merepresentasikan hubungan antar kata pada data saran dengan baik. Saran mahasiswa dapat mengandung kalimat yang sangat panjang, karena itu perpaduan Word2Vec sebagai representasi kata dan CNN dengan teknik convolutional, dapat menghasilkan representasi yang representative dari kalimat panjang tersebut. Penelitian ini menggunakan dua arsitektur CNN, yaitu Simple CNN dan DoubleMax CNN untuk mengidentifikasi pengaruh kompleksitas arsitektur terhadap hasil klasifikasi sentimen.  Berdasarkan hasil pengujian, DoubleMax CNN dapat mengklasifikasi sentimen pada saran mahasiswa dengan sangat baik, yaitu mencapai Akurasi tertinggi sebesar 98%, Recall 97%, Precision 98% dan F1-Score 98%. AbstractStudent’s reviews about department performance can be essential for a college for it can be used to evaluate the department performance and to take an immediate action to improve its performance. This research applies sentiment analysis in the student’s reviews of college department in Politeknik Caltex Riau. Convolutional Neural Network and Word2Vec are employed to analyze the sentiment. CNN is known for its good performance in text classification by applying a convolutional technique to the input sentences. Word2Vec is used as word representation and as an input to the CNN. Word2Vec are dense vectors which can represent the relationship between words excellently. Student’s reviews can be a long sentence; hence the combination of Word2Vec as word representation and CNN with convolutional technique can produce a representative fiture from that long sentence. This research utilizes two CNN architectures, which are Simple CNN dan DoubleMax CNN to identify the effect of the complexity of CNN architecture to final result. Our experiments show that DoubleMax CNN has a great performance in classifying sentiment in the student’s reviews with the best Accuracy value of 98%, Recall 97%, Precision 98% and F1-Score value of 98%.
ANALISA TANDA TANGAN DIGITAL MENGGUNAKAN HEBBIAN LEARNING DAN SUPPORT VECTOR MACHINE Lubis, Juanda Hakim
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 2 No. 2 (2018): Volume 2, Nomor 2, Juli 2018
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v2i2.654

Abstract

Tanda tangan merupakan salah satu ciri dari setiap orang. Biasanya tanda tangan digunakan pada surat pernyataan, atau pun transaksi yang berhubungan dengan hal keuangan, baik penjualan barang maupun pembelian barang. Hal itu menjadi bermasalah jika suatu transaksi gagal karena adanya pemalsuan tanda tangan, tentu saja hal tersebut sangat merugikan, sehingga sangat penting untuk melakukan identifikasi tanda tangan. Untuk menentukan suatu tanda tangan asli atau palsu tersebut digunakan metode Hebbian Learning. Dengan menggunakan Hebbian Learning dan Support Vector Machine tanda tangan akan diekstraksi cirinya lalu dibandingkan dengan tanda tangan uji untuk mengklasifikasikan tanda tangan uji asli atau palsu. Hasil dari proses ini akan menyatakan cocok atau tidak cocok suatu tanda tangan. Bentuk tanda tangan mempengaruhi kombinasi parameter Hebbian Learning untuk mencapai tingkat akurasi yang baik. Dari hasil uji yang dilakukan menunjukkan bahwa sistem dapat mengenali tanda tangan dengan ketepatan rata-rata 91% untuk 5 orang data sampel.
JARINGAN SYARAF TIRUAN PENGENALAN POLA HURUF DENGAN JARINGAN HEBB Muliono, Rizki; Lubis, Juanda Hakim
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 2 No. 1 (2018): Volume 2, Nomor 1, Januari 2018
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v2i1.672

Abstract

Pengenalan pola karakter dalam ilmu artifisial intelegen merupakan metode yang sangat bekembang cepat dan terus dikembangkan hingga saat ini, kebutuhan akan keamanan informasi dan sekuritas sebuah informasi ataupun device bisa di block dengan menggunakan sebuah sistem yang tertanam sebuah kemampuan ataupun fitur dalam pengenalan pola. Model ini bisa diterapkan baik pada pegenalan pola wajah, pola sidik jari. Dalam artikel ini akan di bahas tentang analisi pengenalan pola sederhana dengan menggunakan jaringan syaraf tiruan yaitu metode hebbian. Dengan dua pola awal sebagai pengetahuan atau learningbase nya dan kemudian diuji dengan pola – pola inputan baru yang akan di cek kemiripannya dengan kedua pola learning base apakah di kenali sebagai pola satu atau sebagai pola dua. Dengan menggunakan 25 variabel input dan bias 1 dengan nilai bobot awal 0, pola U dan S di inisialisasi dengan karakter x bernilai 1 dan karakter o bernilai -1 dengan output bipolar, U tareget 1 dan S target -1, fungsi f(net) adalah 1 jika Y >= 0 dan -1 jika Y < 0. Jaringan yang telah terbentuk dari dua pola awal yang telah di kenali kemudian weigh jaringan di test dengan pola 6, 3, U’ dan 8 maka di dapat hasil adalah pola 6 dan 3 dikenali atau mendekati mirip S dan U’ mendekati U.
Co-Authors Akbar, M. Ridho Andre Hasudungan Lubis Dini Hidayatul Qudsi Handayani, Sri Herman Mawengkang Hutapea, Novita Sari Iwan Fitrianto Rahmad Julaysa, Julaysa Khairul Umam Syaliman Khairul Ummi, Khairul Lubis, Akhyar Mesran, Mesran Najwa, Nina Fadilah Napitupulu, Fajrul Malik Aminullah Nurul Khairina Rizki Muliono Sallaby, Achmad Fikri SANWANI SANWANI, SANWANI Siregar, Cindy Astika Sussolaikah, Kelik Utami, Widya Rizki Yuliani, Ega Yuliska, Yuliska