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All Journal Sistemasi: Jurnal Sistem Informasi Creative Information Technology Journal Faktor Exacta Jurnal Mantik Penusa JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology Respati Madani : Indonesian Journal of Civil Society Jurnal Abdi Masyarakat Indonesia Aptekmas : Jurnal Pengabdian Kepada Masyarakat MUALLIMUN : JURNAL KAJIAN PENDIDIKAN DAN KEGURUAN

Norhikmah Norhikmah

Sistem Informasi, Fakultas Ilmu Komputer, Universitas AMIKOM Yogyakarta, Indonesia

Author-ID : 2377405

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KLASIFIKASI PEMINJAMAN BUKU MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION Norhikmah Norhikmah; Rumini Rumini
Sistemasi: Jurnal Sistem Informasi Vol 9, No 1 (2020): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Peminjaman buku merupakan salah satu wujud pelayanan yang diberikan oleh perpustakaan. Peminjaman sangat erat kaitannya dengan persediaan. Pada perpustakaan A dalam menentukan persediaan buku, pegawai perpustakaan kesulitan untuk menentukan jenis-jenis buku apa saja yang sangat dibutuhkan mahasiswa. Dimana penentuan jumlah dan jenis buku belum menggunakan sistem perhitungan yang pasti, hanya berdasarkan perkiraan jumlah mahasiswa dan matakuliah setiap program studi. Maka dari itu dibutuhkan klasifikasi peminjaman jenis buku berdasarkan transaksi peminjaman buku mahasiswa, data yang digunakan dalam penelitian ini yang sebanyak 269.116 dari tahun 2014 sampai 2019 bulan ke 6. Data jenis buku yang diolah sebanyak 184 jenis buku dari 1700 jenis buku, tahapan pertama yang dilakukan melakukan teknik forecasting untuk meramalkan target persediaan setiap jenis buku pada tahun selanjutya, tahapan kedua data transaksi peminjaman buku diproses untuk mengetahui klasifikasi jenis buku dengan menggunakan neural network backpropagation. Didapatkan hasil tingkat error atau MSE sebesar 0,021, menggunakan layer hidden 9 dan fungsi aktivasi tansiq dengan epoch 2000, dengan rekomendasi jumlah jenis buku yang disarankan untuk restock sebanyak 86 jenis buku dengan jumlah prediksi disetiap jenis buku. Tahapan ketiga melakukan pengujian validasi data untuk mengetahui tingkat error klasifikasi dan prediksi, terakhir dilakukan uji regresi menunjukan hasil hubungan yang siqnifikan sebesar 0,006 dengan data variabel yang dujikan yaitu data prediksi, klasifikasi dan target.. Hasil dari penelitian ini adalah dapat memberikan data rekomendasi jenis buku beserta jumlah prediksi di setiap jenis buku yang dibutuhkan ditahun yang akan datang dengan menggunakan metode neural network backpropagation dengan tingkat akurasi sebesar 95,5%.

PERBANDINGAN METODE ARIMA DAN EXPONENTIAL SMOOTHING HOLT-WINTERS UNTUK PERAMALAN DATA KUNJUNGAN Rumini Rumini; Norhikmah Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 9, No 3 (2020): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

ABSTRACTA visit to the creative economy park is a place designed using strategic objectives in collaborating technology capabilities, transferring information and knowledge, planting innovative high-tech companies and entrepreneurs, bringing up new technology industries in the creative economy business to drive economic development. Universitas AMIKOM Yogyakarta has been declared a creative economy park and is known as the Amikom Creative Economy Park (ACEP). ACEP includes several multimedia environments for targeting businesses, for example software development, film, television, games, radio, animation, advertising, investment advisory, and project design. The development of the number of visitors from year to year, predictions need to be made to support the planning and preparation process in receiving visits. The data used in this study are visitor data from January 2019 to December 2019. Analysis of visit prediction data using data mining is the Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing methods. The research resulted from prediction of the number of visit data for 2020 is more accurate using the Holt-Winters exponential smoothing method with a MAPE value of 47,197 when compared to the ARIMA method with a MAPE value of 48,949 so that the MAPE value generated by the ARIMA method is smaller than the Holt-Winters exponential smoothing method. The results of this study are to provide input in the form of predictions of the number of ACEP visitors in the coming year.Keywords: ARIMA, data mining, exponential smoothing, prediction, visitABSTRAKKunjungan di taman ekonomi kreatif adalah meruapakan tempat yang dirancang dengan menggunakan tujuan strategis dalam mengkolaborasikan kemampuan teknologi, transfer informasi dan pengetahuan, penanaman perusahaan teknologi tinggi yang inovatif dan wirausaha, memunculkan industri teknologi baru dalam bisnis ekonomi kreatif untuk mendorong perkembangan ekonomi. Universitas Amikom Yogyakarta telah dinyatakan sebagai taman ekonomi kreatif dan dikenal sebagai Taman Ekonomi Kreatif Amikom (ACEP). ACEP mencakup beberapa lingkungan multimedia untuk membidik bisnis, misalnya pengembangan perangkat lunak, film, televisi, game, radio, animasi, iklan, penasehat investasi, dan desain proyek. Perkembangan jumlah pengunjung dari tahun ke tahun, perlu dilakukan peramalan untuk mendukung proses perencanaan dan persiapan dalam menerima kunjungan. Data yang digunakan dalam penelitian adalah data pengunjung pada Januari 2019 sampai Desember 2019. Analisis data peramalan kunjungan menggunakan data mining yaitu dengan metode Autoregressive Integrated Moving Average (ARIMA) dan Exponential Smoothing. Penelitian yang dihasilkan dari peramalan jumlah data kunjungan untuk tahun 2020 lebih akurat menggunakan metode exponential smoothing Holt-Winters dengan nilai MAPE 47,197 jika dibandingkan metode ARIMA dengan nilai MAPE 48,949 sehingga nilai MAPE yang dihasilkan metode ARIMA lebih kecil dari metode exponential smoothing Holt-Winters. Hasil dari penelitian ini adalah memberikan masukan berupa peramalan jumlah pengunjung ACEP ditahun yang akan datang.Kata Kunci: ARIMA, data mining, exponential smoothing, peramalan, kunjungan

Analysis of Chatbot Response Constancy Using Boyer Moore Algorithm Aldis Gandi Mitra Sanjung; Norhikmah M.Kom (SCOPUS ID: 57216417658)
Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Amikom Computer Club or commonly referred to by the name AMCC is one of the scientific student activity units at Amikom University Yogyakarta. AMCC consists of administrators and members. As the number of AMCC members increases every year, the administrators have difficulty in providing services in answering member questions quickly according to members' needs through the telegram sample chat application. Therefore, a Chatbot system is needed that is built to assist administrators in answering various questions from members using the Boyer Moore Algorithm, by the way the algorithm works that moves to compare characters from right to left or called string matching, thus shortening the information search time. The results of this study are that the chatbot system can respond well to member questions, and the results are tested using the User Acceptence Test, the chatbot only fails to answer 4 questions out of a total of 50 questions, and gets 70% accuracy by testing using the confusion matrix method. AMCC

Implementation of the Fisher-Yates Shuffle Game Algorithm in Learning Hijaiyah Letters Alvien Muhammad Kannabi; Norhikmah M.Kom (SCOPUS ID: 57216417658)
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): 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.v11i3.2053

In 2020 the Indonesian government and the world health agency (WHO) declared the Covid-19 virus a pandemic. As a result, some activities cannot be carried out offline, especially in learning activities. The government requires that learning activities be carried out online or online. The number of children who really need help to understand the learning material provided, for example, is a game which is one of the media in learning for children to make it more interesting in its delivery. With the aim of providing basic Koran education for early childhood with interactive and fun games and the application of the Fisher-Yates Shuffle algorithm in the quiz feature that is used to randomize hijaiyah letter questions. Stages of research 1). Collecting data using library studies, namely Iqra books and related research papers. 2). Game design using story board. 3). Application of the Fisher-Yates Shuffle algorithm. 4). Testing the Fisher-Yates algorithm, black box and question. The results obtained from 10 question and 96 respondents resulted in 89.5% concluding that the hijaiyah letter game uses the Android-based Construct 2 game engine. implementation of the Fisher-Yates Shuffle algorithm on the quiz feature is feasible to use.

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

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

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

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

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

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

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

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