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All Journal Scientific Journal of Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmu Komputer dan Bisnis Jurnal Tekinkom (Teknik Informasi dan Komputer) Innovation in Research of Informatics (INNOVATICS) The Indonesian Journal of Computer Science
Erlinda, Susi
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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PENERAPAN GAMIFICATION HAFALAN ALQURAN DAN HADIS BERBASIS ANDROID MENGGUNAKAN METODE SCOTT Sevutra, Reza; Erlinda, Susi
Jurnal Ilmu Komputer dan Bisnis Vol 10 No 2 (2019)
Publisher : STMIK Dharmapala Riau

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

Abstract

Menghafal adalah suatu aktifitas menanamkan suatu materi di dalam ingatan, sehingga nantinya dapat diingat kembali, sesuai dengan materi yang asli. Salah satu aktivitas menghafal yang biasa anak lakukan adalah menghafal alquran dan hadis. Rendahnya minat anak untuk menghafal alquran dan hadis membuat ustaz/ustazah bingung untuk meningkatkan minat anak dalam menghafal, hal ini karena anak cenderung cepat bosan dalam menghafal alquran maupun hadis yang baru dipelajarinya. Dengan menganut prinsip Gamification mengubah aktivitas menjadi permainan yang menarik. Konsepnya adalah terdapat tujuh level hafalan, setiap level akan terdapat tantangan hafalan yang harus di selesaikan oleh anak. Anak dapat ke level berikutnya jika berhasil menyelesaikan level sebelumnya. disetiap level akan tersedia Point dan ranking jika berhasil menyelesaikan tantangan hafalan. Agar aplikasi menjadi menarik metode yang digunakan adalah SCOTT. Diharapkan dengan aplikasi ini anak dalam melaksanakan aktivitas menghafal alquran dan hadis lebih menyenangkan sambil bermain.
E-Travel Riau Berbasis Mobile Menggunakan Metode Dijkstra Marni, Prina; Asnal, Hadi; Erlinda, Susi; Agustin, Agustin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3261

Abstract

Travel is one of the transportation that is often used by study tours or tourism in Riau Province. Travel is not only engaged in ordering but also in the field of delivery of goods. But what often happens to travel is that bookings are still done manually. The ordering process is carried out by telephone, then the admin records the address of the passenger to be picked up and the admin immediately confirms to the driver to pick up the passenger. The purpose of this study is to assist passengers in ordering travel online and drivers can monitor and determine the location of prospective passengers. In this study, an android-based online travel booking application was created using the Dijkstra algorithm. The dijkstra algorithm is an algorithm used to solve the shortest path problem for a directed graph with non-negative edge weights. This algorithm is used by drivers to determine the fastest route in the process of picking up prospective passengers. The advantage of this Dijkstra method is that it can find the closest route from the starting point to the end point by comparing the smallest value between points that will be used as a route that will be passed by the travel driver in order to get to the destination faster. The results of this study are a travel application that makes it easier for users to book travel and makes it easier for drivers to determine the fastest route in picking up passengers
The Analysis and Optimization of Business Processes for Students in Higher Education Based on Togaf 9.2 Anam, M. Khairul; Nasution, Torkis; Erlinda, Susi; Efrizoni, Lusiana; Susanti, Susanti
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.29952

Abstract

Purpose: The IT Blueprint is used as a guideline in achieving organizational goals, such as the built and development of information technology (IT) infrastructure. STMIK Amik Riau is one of the universities vision to become an excellent university in Sumatra by 2030. To achieve this vision, it is necessary to develop various units, one of which is the built and development of IT in student services. To build IT for student services, an enterprise architecture is needed so that the development is more focused. Study design: In this study, TOGAF became the framework used to design, plan, implement, and manage the company's organizational architecture. TOGAF has 8 phases, but this research takes 6 phases: Architecture Vision, Business Architecture, Information System Architectures, Technology Architecture, Opportunities and Solutions, and Migration Planning. Result: The results obtained in this study are the creation of IT blueprints for student business processes. There are several updates in each process, especially in the information system architecture, then in business processes and technology. There are also updates that need to be done. This study also provides several reasons for updating the Opportunity and Solutions. Other than that, this research guides to apply the updates based on priorities that must be applied to migration planning. Novelty: In the information system architecture, 18 applications become service systems for students. After analyzing it into 31 applications, they will later be used to support good services for students.
Sentiment Analysis of Societal Attitudes Toward the Childfree Lifestyle Using Latent Dirichlet Allocation and Support Vector Machines Husen, Ratna Andini; Agustin, Agustin; Erlinda, Susi; Junadhi, Junadhi; Perumal, Thinagaran
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.12005

Abstract

This research investigates societal perspectives on the childfree lifestyle through Intent Sentiment Analysis, combining Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) techniques. The childfree lifestyle, a deliberate decision by individuals or couples to remain childless, has spurred extensive public discourse, particularly on platforms like Twitter. This research aims to analyze sentiments and intentions within these discussions to uncover their implications for social dynamics and familial relationships. Using LDA, dominant topics were identified from a dataset of Twitter comments on the childfree topic. LDA uncovered hidden themes by modeling topics as mixtures of words, which were subsequently classified into positive, negative, and neutral sentiments using SVM. Data preprocessing included cleaning, tokenization, and stop word removal, while oversampling with SMOTE addressed class imbalances. The optimal number of topics was determined using coherence scores, with the highest coherence value of 0.400 achieved at one topic. The findings revealed that positive sentiments were classified more effectively than negative and neutral sentiments when using LDA and SVM with SMOTE. The top 10 topics primarily reflected societal commentary on the childfree lifestyle. Challenges included incomplete preprocessing, suboptimal clustering of similar themes, and imbalanced data, which limited the effectiveness of topic modeling and classification. Addressing these issues through improved feature selection, parameter optimization, and data augmentation could enhance performance for underrepresented categories. This research provides valuable insights into public attitudes toward the childfree lifestyle, offering implications for social research and policy development in the context of evolving societal norms.  
Aplikasi Mobile Media Pembelajaran Untuk Siswa TK RA Teknologi Menggunakan Metode Fisher Yates Shuffle Rikko kurnia fitra; Erlinda, Susi; T.Sy Eiva Fadha; Hamdani; Andri Setiadi
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3226

Abstract

Penelitian ini bertujuan untuk menghasilkan media pembelajaran untuk pendidikan anak usia dini yang berbasis android, dapat membantu peranan orang tua dalam mendidik anaknya sejak usia dini serta memberikan pengetahuan kepada anak-anak di luar sekolah dengan cara yang menyenangkan melalui aplikasi ini. Metode yang digunakan pada penelitian ini adalah dengan menggunakan model pengembangan aplikasi Multimedia Development Life Cycle (MDLC) yang terdiri dari concept, design, collecting content material, assembly, testing, dan distribution.Dari hasil penelitian yang telah dilakukan adalah aplikasi pengenalan huruf, angka, warna, dan bernyanyi yang berbasis android ini memilik 4 menu utama yaitu, belajar, kuis, dan bermain. Pada menu belajar terdapat pengenalan huruf abjad, angka, warna, dan hewan di menu kuis terdapat pertanyaan dengan kata yang di acak, dan menu bermain terdapat permainan. dari pengujian black box bahwa game edukasi pengenalan huruf dapat berjalan sesuai yang diharapkan, pada pengujian perangkat rata-rata tampilan dan fitur berjalan sesuai yang diharapkan, dan pada pengujian pengacakan soal menggunakan perangkat berbeda telah berjalan sesuai yang diharapkan.
ANALISIS SENTIMEN TERHADAP MASJID RAYA AN-NUR PROVINSI RIAU MENGGUNAKAN TEKNIK STACKING MACHINE LEARNING Malya, Vivi Triani; Erlinda, Susi; Hamdani, Hamdani; Yanti, Rini
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2305

Abstract

This study aims to analyze public sentiment regarding Masjid Raya An-Nur in Riau Province using a machine learning stacking technique. Visitor reviews collected from Google Maps are classified into three categories: Facilities, Cleanliness, and Security. The research applies several preprocessing stages including cleaning, normalization, and tokenization, followed by TF-IDF weighting. To address class imbalance, SMOTE is used before the training process. Three base models—K-Nearest Neighbors (KNN), Decision Tree (DT), and Multinomial Naïve Bayes (MNB)—are trained, and their outputs are combined using Logistic Regression as the meta-classifier in a stacking ensemble. The results show that the stacking model outperforms the individual models with an accuracy of 94%, compared to 73% for KNN, 92.8% for DT, and 83.8% for MNB. The stacking technique provides high and balanced precision, recall, and F1-scores across all sentiment categories. This approach demonstrates the effectiveness of ensemble learning in improving sentiment classification performance for unstructured textual data. The findings are expected to help mosque administrators gain deeper insights into public perceptions and enhance service quality.
INTEGRASI ALGORITMA K-NEAREST NEIGHBORS DAN DECISION TREE UNTUK MEMPREDIKSI HIPERTENSI Aksha, Muhammad Iqbal Al; Yenni, Helda; Erlinda, Susi; Susanti, Susanti
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2306

Abstract

Hypertension is a prevalent health condition and a major risk factor for cardiovascular diseases. Early detection and management are essential to prevent complications. This study aims to optimize the accuracy and stability of hypertension risk prediction by applying a stacked ensemble technique that combines multiple base classifiers—K-Nearest Neighbors (KNN) and Decision Tree (DT)—with Logistic Regression as the meta-learner. The dataset used was imbalanced, thus requiring class balancing with the Synthetic Minority Over-sampling Technique (SMOTE), along with data preprocessing and scaling. The study applies a quantitative approach to train and evaluate models using Python. Results demonstrate that the stacked ensemble model achieves superior performance compared to individual classifiers, with a maximum accuracy of 74.52%. These findings indicate that the combination of different classifiers through ensemble stacking enhances the reliability and predictive capability of hypertension detection models. The approach offers potential value for improving early diagnosis and supporting clinical decision-making.
PREDIKSI WAKTU KELULUSAN MAHASISWA BERDASARKAN FAKTOR AKADEMIK DAN DEMOGRAFIS MENGGUNAKAN RANDOM FOREST DAN XGBOOST Supahri, Hafid Azis; Erlinda, Susi; Nasution, Torkis; Asnal, Hadi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2308

Abstract

Accurate graduation time is an important measure to illuminate how well the higher education system functions. Data from 10,000 students was used, including GPA, credits, age, gender, place of residence, employment status, economic status, and scholarship acceptance. Class imbalance in the data is addressed through the CRISP-DM and SMOTE methods. The evaluation results show that both algorithms have the capability to predict permit status with high accuracy; Random Forest achieved an accuracy of 91.95% and XGBoost 91.85%. Based on the precision, recall, and F1 score, both models demonstrate very good and balanced performance, with Random Forest being slightly superior in result stability. Therefore, Random Forest is recommended as the best model for graduation prediction. This research is expected to help colleges identify students who may graduate late to provide timely interventions.
Co-Authors Agustin Agustin Agustin Aksha, Muhammad Iqbal Al Anam, M Khairul Andri Setiadi Efrizoni, Lusiana Hadi Asnal, Hadi Hamdani Hamdani Hamdani Helda Yenni, Helda Husen, Ratna Andini Junadhi, Junadhi Malya, Vivi Triani Marni, Prina Perumal, Thinagaran Rikko kurnia fitra Rini Yanti, Rini Sevutra, Reza Supahri, Hafid Azis Susanti Susanti Susanti, Susanti T.Sy Eiva Fadha Torkis Nasution