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Norhikmah Norhikmah
Sistem Informasi, Fakultas Ilmu Komputer, Universitas AMIKOM Yogyakarta, Indonesia
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Comparison of Phishing Detection Tests using the SVM Method with RBF and Linear Kernels Rumini Rumini; Norhikmah Norhikmah; Ali Mustofa; Sulistyo Pradana
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2882

Abstract

Phising adalah sebuah tindakan kriminal untuk mencuri informasi pribadi orang lain menggunakan entitas electronic, salah satunya adalah website. Informasi ini dicuri dari website yang telah diakses yang mengandung phising atau dengan kata lain masuk ke dalam kategori website phising. Tujuan dari web phising adalah membuat pengguna percaya bahwa mereka berinteraksi dengan situs resmi. Umumnya informasi yang dicari phisher (pelaku phising) adalah berupa username, password, baik itu akun media sosial atau akun nomor kartu kredit dengan cara diarahkan ke sebuah situs website palsu. Maka dari itu perlu adanya deteksi web phising yang berguna untuk melindungi user dari tindak pencurian informasi pengguna. Penelitian ini membahas dua kernel dalam metode SVM (Support Vector Machine) untuk deteksi web phising yaitu kernel RBF (Radial Basis Function) dan kernel linear. Akurasi yang didapatkan dengan ketiga kernel menghasilkan nilai akurasi yang berbeda-beda. Hasil akurasi pengujian sistem deketksi web phising dengan Kernel Linear sebesar 92.582 % dan Kernel Radial Basis Function sebesar 96.426 %. Akurasi paling tinggi dengan metode SVM untuk deteksi web phising yaitu menggunakan kernel RBF (Radial Basis Function).
Optimizing the Profile Matching Algorithm using the Analytical Hierarchy Process in the Selection of Teaching Assistants Nita Helmawati; Norhikmah Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3172

Abstract

The selection of the best practicum assistants is traditionally done through a conventional method, which involves voting by active students attending lab classes. However, upon evaluation, it was found that the results were not accurate. Some cases revealed that assistants were chosen based solely on popularity or recognition among the students, possibly influenced by physical appearance or public speaking skills in front of the class, while other important aspects were not considered. This situation could lead to social jealousy. The problem lies in the difficulty of combining evaluation criteria and determining the relative weights for each criterion in the process of selecting the best practicum assistants at the college. Additionally, there is a lack of objectivity in decision-making during the selection process, resulting in an unstructured and immature decision-making process. Therefore, this research aims to enhance the process of selecting the best practicum assistants at the college through optimizing the profile matching algorithm using the Analytic Hierarchy Process (AHP) method. AHP's role involves checking the weights and making paired comparisons to evaluate each criterion and determine the criterion weights. AHP is also utilized to ensure consistency in determining the weights. On the other hand, the role of profile matching is to provide accurate rankings or comparisons based on the suitability scores between the profiles of potential assistants and the reference profile. The combination of these two algorithms is expected to result in a more accurate selection of practicum assistants by effectively measuring the decision criteria weights. Therefore, the difficulty of combining evaluation criteria and determining the relative weights for each criterion can be minimized. Furthermore, optimizing the profile matching algorithm will enable a more objective decision-making process for selecting the best practicum assistants through more accurate rankings or comparisons based on the suitability scores with the reference profile. Based on this optimization, the collaboration of the two algorithms can achieve comparison results with an accuracy rate of 90%.
Rare Animal Recognition Applicaton Using Augmented Reality Technology And Marker Based Qr Code Fintas Yulianti; Norhikmah Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3361

Abstract

Indonesia has a rich diversity of animals, but the lack of media to introduce endangered species from various regions such as Java, Sulawesi, Bali, NTT and Kalimantan has led to a lack of awareness of their existence, which has led to the threat of extinction. The purpose of this research is to produce an Android application that utilizes Augmented Reality (AR) technology to introduce endangered species. The MDLC (Multimedia Development Life Cycle) method is used in this analysis, which consists of 6 stages: concept, design, collection of materials, assembly, testing, and finally distribution. The Unity Engine is used as the basis for Android applications. Augmented Reality (AR) is an effective learning tool for early childhood education, especially in the realm of endangered species education. In addition to introducing rare animals, the app will include quizzes as part of the game to increase knowledge about these animals.
Management of Tracking in Real Time on a Website-based Laundry Information System Dewi Pratama Mastha Cahyaningrum; Norhikmah Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2943

Abstract

Dewi Jaya Laundry provides a variety of laundry services. However, the administrative process and transactions with customers are still not computerized, so it can take more time and customers cannot do tracking of their clothes. To overcome these problems, a solution was built in the form of a website-based information system that has a Real Time Tracking feature. Primary data collection was obtained directly from the research site, namely Dewi Jaya Laundry located on Perumnas street by making direct observations at Dewi Jaya Laundry. Meanwhile, we gather secondary knowledge from scientific articles and books on database development for websites. It utilizes Waterfall research methodology and is built with PHP and MySQL databases. This system is built through the stages of analysis, design, development, and testing. The black box method is used for system testing, which emphasizes the functionality of the system. The system's ability to perform its intended functions has been verified by the test results. This research succeeded in developing a laundry information system that has a Real Time Tracking feature that functions to monitor customer laundry packages, so customers can know which stage of the washing or drying process is taking place, so they can know when clothes are ready to be taken.
Implementation of the Levenshtein Distance Algorithm and the Regular Search Expression Method for Detecting Typors in Javascript Mu’alif Lihawa; Anggit Dwi Hartanto; Norhikmah Norhikmah; Donni Prabowo; Ika Nur Fajri; Wiwi Widayani
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2795

Abstract

Typing is an activity to write an article in printed form that has been assembled by a typewriter. With the rapid development of the times, typewriters were replaced by computers because they were efficient in making writing or text. a text or writing that is easy to understand in conveying information does not have word mistakes that result in unclear information being conveyed. In word processing applications such as Microsoft Office Word, it has the word suggestions and autocorrect word features which are very useful in checking an article where there are word errors in the writing. This research develops a javascript library to detect typo errors for writing wrong words and recommends the right words to change the wrong words. This study uses the Levenshtein Distance Algorithm and the Regular Search Expression method. The results of this study were successfully applied to the word recommendation feature in the library with an accuracy value of 50% and a precision level of 5%.
Sentiment Analysis using the Support Vector Machine Algorithm on Covid_19 Adytyo Wahyu Nugroho; Norhikmah Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.3778

Abstract

This massive development of information technology makes it easier for people's lives in various fields, one of them is social media, social media that people use a lot to get information about news or events that are happening in Indonesia, one of which is social media Twitter which provides a lot of information for the people of Indonesia, one of which is information about Covid-19 which is currently rife in the territory of Indonesia Sentiment analysis is a branch of Natural Language Processing (NLP) which can help determine the sentiments that occur in society. This study uses data in the form of tweets to carry out sentiment analysis obtained on Twitter social media.This research utilizes one of the Supervised Learning algorithms, namely Support Vector Machine. In this study, three (3) kernels are used for the Support Vector Machine, each of which is Linear, Radial basis function and Polynomial, to find which kernel produces the highest accuracy value. From the experiments carried out using data sharing for training as much as 70% and for testing data as much as 30% of the total data of 6000 data, the resulting accuracy value for the Support Vector Machine method on the Linear kernel produces an accuracy value of 89% and for the Radial kernel base function accuracy by 90% and for the Polynomial kernel it produces an accuracy of 88%. So it is concluded for the three (3) kernels for testing the Support Vector Machine method on the Radial basis function kernel to produce the best accuracy value
Co-Authors Acihmah Sidauruk Adytyo Wahyu Nugroho Aldis Gandi Mitra Sanjung Ali Mustofa Alvien Muhammad Kannabi Anggit Dwi Hartanto Cut Tesya Iftillah Anggun Pratiwi Dewi Pratama Mastha Cahyaningrum Donni Prabowo Fintas Yulianti Ika Nur Fajri Kusrini Kusrini M. Rudyanto Arief Mu’alif Lihawa Neela Afifah Nita Helmawati Nita Helmawati Nor Latifah Nurlaila Nurlaila Randani Randani Rifqhy Rayhan Andi Riga Pratama Rumini Rumini . Rumini Rumini Sulistyo Pradana Vidyana Arsanti Wiji Nurastuti Wiwi Widayani