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All Journal Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Data Science: Journal of Computing and Applied Informatics International Journal Software Engineering and Computer Science (IJSECS) INJURITY: Journal of Interdisciplinary Studies J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence)
Mutiawani, Viska
Jurusan Informatika, FMIPA, Universitas Syiah Kuala Universitas Syiah Kuala, Jl. Syech Abdurrauf No.3, Darussalam, Banda Aceh 23111
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Public Health Social Sciences

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HASHTABLE SEBAGAI ALTERNATIF DARI ALGORITMA PENCARIAN BINER PADA APLIKASI E-ACESIA Mutiawani, Viska
Jurnal Informatika Vol 8, No 2 (2014): Juli
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (139.192 KB) | DOI: 10.26555/jifo.v8i2.a2060

Abstract

Aplikasi e-Acesia merupakan kamus dwibahasa Aceh-Indonesia yang dapat digunakan pada telepon genggam berbasis Java MIDP (Mobile Information Device Profile). Aplikasi kamus ini menyimpan data berupa kata dan terjemahannya dalam file teks. Proses utama pada kamus adalah proses pencarian. Aplikasi ini mencoba dua jenis pencarian yaitu pencarian biner dan pencarian pada struktur data Hashtable. Kedua algoritma ini dipilih karena data kamus yang terurut dan tetap serta algoritmanya mudah diimplementasikan pada Java MIDP yang memiliki jumlah Class terbatas. Pengujian terhadap kedua-dua algoritma menggunakan file teks berisi jumlah kata 1000, 2000, 3000 dan 4000 kata. Pengujian pada emulator di komputer menghasilkan waktu pencarian yang sama untuk kedua-dua algoritma yaitu 0 milidetik. Sedangkan pengujian pada telepon genggam dengan menggunakan algoritma pencarian biner menghasilkan waktu 0 milidetik untuk 1000 kata, 0.042 milidetik untuk 2000 kata dan 0.125 milidetik untuk 3000 dan 4000 kata. Sebaliknya waktu pencarian pada telepon genggam dengan menggunakan struktur data Hashtabel menghasilkan waktu rata-rata pencarian yang konstan yaitu 0 milidetik. Namun demikian ukuran milidetik adalah sangat kecil dan tidak terdeteksi oleh pengguna aplikasi. Selain waktu pencarian, pengujian juga mendata besarnya ukuran file jar. Ternyata ukuran file jar bertambah berdasarkan jumlah kata yang disimpan dalam file teks dan ukurannya sama untuk kedua-dua algoritma. Struktur data Hashtable ternyata dapat menjadi alternatif struktur data dan algoritma pada aplikasi kamus e-Acesia karena waktu pencarian yang konstan dan dapat menampung data yang lebih banyak berbanding dengan struktur data array pada pencarian biner.Kata kunci : aplikasi kamus, telepon genggam, Java MIDP, pencarian biner, Hashtable
Design and Development of a Web-Based Complaints Management System at Syiah Kuala University Integrated Laboratory Mutiawani, Viska; Hafist, Pilar Al; Dawood, Rahmad
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2398

Abstract

Complaint handling is essential to Syiah Kuala University’s Integrated Laboratory Technical Management Unit (ILTMU) services. However, manual complaint handling at ILTMU requires more time and energy and increases the staff workload. Complaint handling is necessary to continuously improve the performance efficiency and service quality provided by ILTMU. Therefore, to solve the problem of handling complaints manually, this research intends to design and develop a web-based complaint management system at ILTMU of Syiah Kuala University. The proposed system uses the Goal-Directed Design method, while the development process follows Scrum. The system was developed using Flask and some services from the Google Cloud Platform, namely, the Google App Engine and Google Cloud Datastore. The system is divided into four unique user groups. Each has access privileges and responsibilities: operators, Management, Follow-up, and Customers. Each of these user groups has its distinctive features. The entire features of this application were tested using both Black Box and White Box testing. All system functions were successfully executed using the Black Box testing technique. Meanwhile, the White Box testing returned a value of 100\% passed, indicating that the function being tested is error-free. A usability test was conducted with 20 respondents representing each user group using the USE Questionnaire on a 7-point Likert scale. The usability test results gave an overall average value of 6.13, which suggests that people have a positive attitude toward the system and that it is practical, easy to use, easy to learn, and satisfying.
Pengaruh Tahapan Preprocessing Terhadap Model Indobert Dan Indobertweet Untuk Mendeteksi Emosi Pada Komentar Akun Berita Instagram Khairani, Ulfia; Mutiawani, Viska; Ahmadian, Hendri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1148315

Abstract

Platform media sosial seperti Instagram telah membentuk ruang di mana berita dapat dengan mudah ditemukan dan menarik perhatian individu. Pada Instagram, dapat memberikan komentar-komentar terhadap berita yang telah dibaca. Pemahaman terhadap emosi yang mengiringi komentar-komentar yang telah diberikan pengguna pada postingan berita dapat membantu memahami bagaimana berita tersebut diserap, diinterpretasi, dan direspons oleh publik. Penelitian ini mengkategorikan empat emosi yaitu marah, senang, takut, dan sedih dengan menggunakan model terlatih IndoBERT dan IndoBERTweet. Penelitian ini bertujuan untuk membandingkan model IndoBERT dan IndoBERTweet dalam mendeteksi emosi pada komentar akun berita Instagram dan mengeksplorasi dampak penggunaan tahapan preprocessing khususnya remove stopwords dan stemming pada kedua model. Hasil penelitian menunjukkan bahwa model yang tidak melalui tahapan remove stopwords dan stemming menghasilkan kinerja yang lebih baik dibandingkan model yang melalui tahapan remove stopwords dan stemming, dengan perolehan akurasi sebesar 92,54% untuk model IndoBERTweet dan 88,81% untuk model IndoBERT.   Abstract   Social media platforms such as Instagram have created a space where news can be easily discovered and attract the attention of individuals. On Instagram, people can provide comments on the news they have read. Understanding the emotions that accompany the comments that users have given on news posts can help understand how the news is absorbed, interpreted and responded to by the public. This research categorizes four emotions, anger, happiness, fear and sadness, using pre-trained models IndoBERT and IndoBERTweet. This research aims to compare the IndoBERT and IndoBERTweet models in detecting emotions in Instagram news account comments and explore the impact of preprocessing stages, especially removing stopwords and stemming on both models. The research results showed that the model that did not go through the remove stopwords and stemming stages produced better performance than the model that went through the remove stopwords and stemming stages, with an accuracy of 92.54% for the IndoBERTweet model and 88.81% for the IndoBERT model.
Accuracy Comparison of Data Mining Methods for Internet Gaming Disorder Classification Juwita, Juwita; Junidar, Junidar; Mutiawani, Viska; Nurdin, Zahnur; Zainuddin, Zul Ikram
INJURITY: Journal of Interdisciplinary Studies Vol. 2 No. 4 (2023): INJURITY: Journal of Interdisciplinary Studies.
Publisher : Pusat Publikasi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58631/injurity.v2i4.62

Abstract

Excessive access to online games by players can lead to addiction. Online game addiction can be detected through various ways including self-report questionnaires. Based on the value of the answers in the questionnaire a person can be categorized into groups, mild addiction, moderate addiction and severe addiction. Generally, this assessment calculation is done manually or through procedural programming languages. This is not efficient for processing more and more data, for that the mechine learning classification model is applied to solve the problem of program code repetition. This study compared the performance of three mechine learning methods against two different types of questionnaires, namely questionnaires with Likert scale and questionnaires with yes no type. The case study used in this study is online game addiction among high school students in Banda Aceh City, Indonesia. This research successfully proved that the algorithm ... It is better to use questionnaires with data types...., while algorithms....are better to use for questionnaires with types.....with the accuracy of the three algorithms are as follows. This study reveals the emergence of online game addiction, especially among high school students within Banda Aceh city. The results depicted that as many as 6% of high school students in the city of Banda Aceh indicated experiencing online game addiction based on their reports. Another objective of this research is to find the best accuracy between Naive Bayes and Support vector machine (SVM) in classifying the severity of the online game. It found that SVM accuracy was higher than Naive Bayes for the case of online game addiction level classification in high school students in Banda Aceh. This study provided baseline data for further research.
Rancang Bangun Aplikasi Pengujian Usability Berbasis Web Zulfidiana, Zulfidiana; Yunardi, Dalila Husna; Mutiawani, Viska
J-SIGN (Journal of Informatics, Information System, and Artificial Intelligence) Vol 1, No 01 (2023): May
Publisher : Department of Informatics, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/j-sign.v1i01.31805

Abstract

Usability is one factor that influences software design success during the software testing process. Usability testing is used to evaluate whether the software meets user needs or not. Usability testing is usually carried out using a certain questionnaire method, but the distribution and measurement of the results are carried out separately. This makes the researchers spend more time testing the application. Therefore, this study provides a solution by designing and building web-based applications to measure the usability of a software design or product with the SUS, UMUX, and UMUX-lite questionnaires. The development method used in this study is Rapid Application Development (RAD) with the stages of the process starting with problem identification, literature study, needs planning, RAD design workshops, implementation, and report writing. The application was built using the Laravel framework and the database using MySQL. The main features of the online questionnaire application were the creation of a questionnaire and the analysis of the results of the questionnaire. The functionality of the application was tested by using the Black Box testing method. Then the developed application was also utilized in usability testing using the SUS, UMUX, and UMUX-lite questionnaires. The results of application usability testing using the SUS, UMUX, and UMUX-Lite methods obtained a final score of 88; 91,28, and 82,05 where all these values mean the application can be accepted and used. Keywords: Usability Testing, Rapid Application Development, SUS, UMUX, UMUX-Lite
Identification Of Malaria Parasites Plasmodium Vivax on Red Blood Cells Using the Probabilistic Neural Network Method Mutiawani, Viska
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i2-22535

Abstract

Malaria is a disease that is infects human red blood cells transmitted through the bite of a female Anopheles mosquito that contains the parasite genus Plasmodium. Plasmodium vivax is one of the types of parasites that causes malaria, which is known as the type of malaria with the widest distribution area, from tropical, subtropical to cold climates. The diagnosis of malaria, basically depend on microscopic analysis of Giemsa-smeared thin and thick films of blood. However, this diagnostic method is time consuming and prone to human error. To overcome this problem, a method is needed to automatically identify malaria parasites on red blood cells. This study proposes to identifying the malaria parasite Plasmodium vivax using the Probabilistic Neural Network method. The steps taken before identification are preprocessing using Green Channel, Contrast Limited Adaptive Histogram Equalization (CLAHE), Morphological Close and Background Exclusion, then segmentation with Otsu Thresholding, next step is post- processing with Connected Component Analyst (CCA) and feature extraction with Invariant Moment. The results of this research showed that the method used was able to identify the malaria parasite plasmodium vivax on microscopic images of reb blood cells with an accuracy rate of 97.14%, and sensitivity of 95%.
Co-Authors Hafist, Pilar Al Hendri Ahmadian Junidar, Junidar Juwita Juwita, Juwita Khairani, Ulfia Nurdin, Zahnur Rahmad Dawood Yunardi, Dalila Husna Zainuddin, Zul Ikram Zulfidiana, Zulfidiana