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Teknik Pencegahan Penetrasi SQL Injeksi Dengan Pengaturan Input Type Number dan Batasan Input Pada Form Login Website Sahat Parulian Sitorus; Rahmad Aditiya Habibi
U-NET Jurnal Teknik Informatika Vol. 4 No. 2 (2020): U-NET Jurnal Teknik Informatika | Agustus
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52332/u-net.v4i2.303

Dalam dunia IT pada website sangat rentan akan serangan hacker dengan berbagai jenis cara agar mereka dapat membobol keamanan website target. Serangan SQL Injeksi sering dilakukan pada pembobolan website dari form login dengan menginputkan username dan password khusus injeksi sehingga website dapat dibobol dengan mudah. Dalam mengamankan sebuah website dari serangan injeksi beragam caranya salah satunya dengan menggunakan teknik maxlength dan input type number. Teknik maxlengntht dan input type number ini dibuat dalam bentuk source code php atau html yang disisipkan kedalam source code form login pada bagian input username dan password. Salah satu keunggulan teknik maxlengnth dan input type ini akan membuat batasan inputan username dan mengubah format inputan password hanya bertipekan angka saja yang artinya akan mencegah hacker dalam melakukan penetrasi secara paksa dalam serangan SQL Injeksi pada website.

Rancang Bangun Sistem Informasi Desa Pada Kelurahan Lingga Tiga Kabupaten Labuhanbatu Berbasis Web Rahmad Aditiya
U-NET Jurnal Teknik Informatika Vol. 5 No. 1 (2021): U-NET Jurnal Teknik Informatika | Februari
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52332/u-net.v5i1.337

Sistem Informasi desa merupakan salah satu tugas yang harus dilaksanakan oleh Kelurahan Lingga Tiga, Kecamatan Bilah Hulu, Kabupaten Labuhanbatu, Kota Rantauprapat. Sistem Informasi desa bertujuan untuk melakukan pencatatan segala hal menyangkut data penduduk seperti jumlah penduduk, status penduduk, keadaan geografis penduduk, mortalitas, mobilitas, sumber daya manusia dan potensi sosial ekonomi penduduk sebagai salah satu aspek penting dalam pembangunan nasional jangka panjang. Sistem informasi desa harus dilakukan secara terus-menerus, berkesinambungan, tepat waktu dan akurat. Oleh karenanya diperlukan dukungan sistem komputerisasi untuk dapat membantu manusia dalam pekerjaan tersebut. Komputer akan diintegrasikan dengan sumber daya manusia, basisdata dan prosedur-prosedur yang dibutuhkan sehingga dapat mewujudkan sebuah sistem informasi yang dapat mengatasi berbagai persoalan yang terkait dengan pengolahan dan administrasi data desa. Tujuan penelitian ini untuk merancang dan membangun sebuah sistem informasi data desa. Hasil penelitian ini menunjukkan bahwa penggunaan sistem informasi data desa ini dapat membantu pengelolaan dan pendataan data desa dan penduduknya dengan tepat, cepat dan akurat sesuai yang diharapkan.

Implementasi Forward Chaining untuk Mendiagnosis Penyakit Kulit Dermatitis pada Bayi Betty Rahmaditiya; Tatang Rohana; Santi Lestari
Scientific Student Journal for Information, Technology and Science Vol. 3 No. 2 (2022): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pengetahuan orangtua akan gejala-gejala yang dialami oleh bayi ketika mengalami suatu keluhan gejala dermatitis menjadi sangat penting. Cara untuk mendiagnosis penyakit kulit dermatitis pada bayi dengan memanfaatkan teknologi kecerdasan buatan yaitu sistem pakar. Tujuan dari penelitian ini adalah untuk mendiagnosis penyakit kulit dermatitis pada bayi dengan menggunakan metode algoritme forward chaining untuk variabel input pada sistem. Metode forward chaining ini dipilih untuk mendiagnosis penyakit kulit dermatitis pada bayi diperlukan gejala (fakta- fakta) IF terlebih dahulu, lalu dilanjutkan dengan THEN yang berisi kesimpulan. Implementasi dari sistem pakar penyakit kulit dermatitis ini telah diuji dengan 20 data. 17 data didapatkan melalui kuesioner dan 3 data dari kasus-kasus yang ada di internet. Berdasarkan hasil pengujian yang dilakukan tersebut, menghasilkan tingkat akurasi sebesar 80% yang menunjukan bahwa aplikasi berfungsi dengan baik sesuai dengan diagnosa pakar

Prediksi Tingkat Ketersediaan Stock Sembako Menggunakan Algoritma FP-Growth dalam Meningkatkan Penjualan Rahmad Aditiya; Sarjon Defit
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (September 2020)
Publisher : SAFE-Network

Large data sets can be processed to become useful information, one of the data that can be processed is sales transaction data at UD. Smart Aliwansyah, which will become important information to increase sales. This study aims to find the pattern of product purchases to predict the level of availability of staple foods so as to increase sales. The data that is processed in this study uses the sales transaction data of goods obtained from the sales invoice of UD. Smart Aliwansyah, North Sumatra Tax Village. Based on these data, with the provision that a minimum of 2 types of goods in 1 transaction is examined using a data mining technique in association with the FP-Growth algorithm with a confidence value of 75% and a minimum support of 20%. The tools used by Rapidminer 9.4 are to obtain product purchasing patterns which are used as information to predict the level of stock availability. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing basic food stocks to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by grocery stores.

BERTopic Modeling of Natural Language Processing Abstracts: Thematic Structure and Trajectory Samsir Samsir; Reagan Surbakti Saragih; Selamat Subagio; Rahmad Aditiya; Ronal Watrianthos
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6426

The rapid growth in the academic literature presents challenges in identifying relevant studies. This research aimed to apply unsupervised clustering techniques to 13,027 Scopus abstracts to uncover structure and themes in natural language processing (NLP) publications. Abstracts were pre-processed with tokenization, lemmatization, and vectorization. The BERTopic algorithm was used for clustering, using the MiniLM-L6-v2 embedding model and a minimum topic size of 50. Quantitative analysis revealed eight main topics, with sizes ranging from 205 to 4089 abstracts per topic. The language models topic was most prominent with 4089 abstracts. The topics were evaluated using coherence scores between 0.42 and 0.58, indicating meaningful themes. Keywords and sample documents provided interpretable topic representations. The results showcase the ability to produce coherent topics and capture connections between NLP studies. Clustering supports focused browsing and identification of relevant literature. Unlike human-curated classifications, the unsupervised data-driven approach prevents bias. Given the need to understand research trends, clustering abstracts enables efficient knowledge discovery from scientific corpora. This methodology can be applied to various datasets and fields to uncover overlooked patterns. The ability to adjust parameters allows for customized analysis. In general, unsupervised clustering provides a versatile framework for navigating, summarizing, and analyzing academic literature as volumes expand exponentially.

Evaluation of the moodle-based learning system applying the end user computing satisfaction method Ritonga, Wahyu Azhar; Dalimunthe, Abdul Hakim; Aditiya, Rahmad; Ritonga, Sangkot Idris
Jurnal Inovasi dan Teknologi Pembelajaran Vol 10, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um031v10i12023p106

Abstrak: Pembelajaran elektronik dengan memanfaatkan perangkat teknologi seperti komputer, tablet maupun telefon pintar telah berkembang pesat pasca pandemic COVID-19 namun belum menghasilkan temuan yang konklusif. Maka, tujuan penelitian ini untuk menginvestigasi minat siswa terhadap sistem pembelajaran elektronik berbasis Moodle. Metode yang digunakan End User Computing Satisfaction (EUCS) dengan pengujian sistem pada Moodle dengan sampel penelitian 70 siswa. Moodle merupakan sistem pengelolaan pembelajaran elektronik dimana guru dapat menyusun mata pelajaran, membuat group kelas, memberikan materi (mengunggah materi), memberikan tugas baik berbentuk ujian, kuis, tes, mengumpulkan tugas, menilai tugas yang dapat digunakan secara sinkronus maupun asinkronus. Hasil uji EUCS menunjukkan bahwa setelah menggunakan pembelajaran elektronik minat siswa mengalami peningkatan sebesar 86,34%. Selain itu capaian hasil belajar siswa juga mengalami peningkatan signifikan. Penerapan e-learning berbasis Moodle meningkatkan minat siswa dalam belajar karena siswa dapat mengakses kapanpun dan dimanapun. Abstract: Electronic learning using is utilizing technological devices such as computers, tablets, and smartphones has developed rapidly after the COVID-19 pandemic but has not produced conclusive findings. Thus, the purpose of this learning is to investigate students' interest in Moodle-based electronic learning systems. The method used is End User Computing Satisfaction (EUCS) by testing the system on Moodle with a research sample of 70 students. Moodle is an electronic learning management system where teachers can arrange subjects, create class groups, provide material (upload material), give assignments in the form of exams, quizzes, and tests, collect evaluations, and assess assignments that can be used synchronously or asynchronously. EUCS test results show that after using electronic learning, students’ interest has increased by 86.34%. In addition, student achievement also experienced a significant increase. The application of Moodle-based e-learning increases students' interest in learning because students can access it anytime and anywhere.

Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database Samsir, Samsir; Kusmanto, Kusmanto; Dalimunthe, Abdul Hakim; Aditiya, Rahmad; Watrianthos, Ronal
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

A film review is a subjective opinion of someone who has different feelings about each film. As a result, film enthusiasts will struggle to assess whether the film meets their requirements. Based on these issues, sentiment analysis is the best way to fix them. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text contains positive or negative thoughts. The Nave Bayes method was chosen because it can classify data based on the computation of each class's probability against objects in a given data sample. The best model was created utilizing data without lemmatization, 500 vector sizes, and Nave Bayes classification, with an accuracy of 78.96 percent and a f1-score of 78.81 percent. Changes in vector size affect the system's capacity to foresee positive and negative sentiments. The difference in accuracy and recall values shows that when vector size 300 is utilized, the precision and recall outcomes are lower than when vector size 500 is used.

Prediksi Tingkat Ketersediaan Stock Sembako Menggunakan Algoritma FP-Growth dalam Meningkatkan Penjualan Aditiya, Rahmad; Defit, Sarjon
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (September 2020)
Publisher : SAFE-Network

Large data sets can be processed to become useful information, one of the data that can be processed is sales transaction data at UD. Smart Aliwansyah, which will become important information to increase sales. This study aims to find the pattern of product purchases to predict the level of availability of staple foods so as to increase sales. The data that is processed in this study uses the sales transaction data of goods obtained from the sales invoice of UD. Smart Aliwansyah, North Sumatra Tax Village. Based on these data, with the provision that a minimum of 2 types of goods in 1 transaction is examined using a data mining technique in association with the FP-Growth algorithm with a confidence value of 75% and a minimum support of 20%. The tools used by Rapidminer 9.4 are to obtain product purchasing patterns which are used as information to predict the level of stock availability. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing basic food stocks to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by grocery stores.

Machine Learning-Driven Sentiment Analysis of Social Media Data in the 2024 U.S. Presidential Race Samsir, Samsir; Ritonga, Wahyu Azhar; Aditiya, Rahmad; Watrianthos, Ronal
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1762

This study investigates public sentiment patterns during the 2024 U.S. Presidential Race through machine learning analysis of social media data from X (formerly Twitter). Using a dataset of 500 annotated tweets collected from Kaggle, we employ BERT-based sentiment analysis, temporal engagement tracking, and Latent Dirichlet Allocation (LDA) topic modeling to examine discourse across five major candidates. The analysis reveals predominantly positive sentiment (54.2%) in political discussions, with established party candidates receiving higher positive engagement. Temporal analysis demonstrates strong correlations between major campaign events and public engagement, with presidential debates generating peak interaction levels. Topic modeling identifies five key themes driving voter discourse: economic policy, healthcare, climate change, social justice, and foreign policy. Positive content consistently achieved 20-30% higher engagement rates than negative content, though negative sentiments showed sharp spikes during controversies. Our findings contribute to understanding digital political discourse dynamics and offer practical insights for campaign strategy in the social media era. The study's limitations include platform-specific constraints and a two-month observation period, suggesting opportunities for cross-platform analysis in future research.

PELATIHAN SOLUSI INOVATIF UNTUK MENINGKATKAN KEMAMPUAN MENULIS ARTIKEL ILMIAH MELALUI PENDEKATAN ARTIFICIAL INTELEGENSI (AI) Ritonga, Wahyu Azhar; Aditiya, Rahmad; Syafriyeti, Rahmi; Megawati, Betti; Ritonga, Maisaroh; Nursalimah
J-COSCIS : Journal of Computer Science Community Service Vol. 5 No. 2 (2025): J-COSCIS : Journal of Computer Science Community Service
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/bk0mce44

Scientific article writing training is an essential skill for students and academics in conveying their research findings and scientific thinking. However, various challenges such as limited academic vocabulary, writing structure, and time often become obstacles. This study aims to develop effective writing strategies and evaluate Artificial Intelligence (AI)-based training as an innovative solution to improve the quality and productivity of scientific article writing skills and understand the ethics of using AI in academic writing. The method used was a quasi-experimental model with a structured training model, utilizing AI-based tools such as ChatGPT, Grammarly, and SciSpace. The results showed a significant increase in the quality and productivity of the training participants' writing. This training provides evidence that an AI approach can be an effective tool in academic literacy.

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