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Implementation of SAW Method for Design and Development Apartment Recommendation System in Tangerang Using Mobile-Based Nugraha, Achmad Ilyasa; Kusnadi, Adhi; Tobing, Fenina Adline Twince
ULTIMATICS Vol 15 No 2 (2023): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v15i2.3492

The house is no longer the sole type of residence available while looking for a place to live. Apartments are a solution for those who need a place to live in locations with limited land, such as Tangerang, in today’s period. However, criteria are needed to choose an apartment based on a person’s needs, thus in this project, we will develop and create an apartment recommendation system in Tangerang using the SAW approach to make it easy for people to choose the best apartment. The user’s choice will be determined by the recommendation system based on their interests, activity, and other data. To put the recommendation system into action, the FMADM method must be employed. A Simple Additive Weighing (SAW) approach is required to complete this FMADM, which is a mechanism for computing the number of performance appraisals for each alternative based on all criteria. This recommendation system is called APARTKU, and it was created with HTML5, CSS, and AngularJS, as well as the Ionic Framework and the Firebase Database. The system was then put to the test by administering questionnaires to 32 respondents using the DeLone and McLean methodologies, and the results were tallied using the Likert Scale method, yielding a score of 90.64 percent, based on the interval on the Likert Scale technique, these results imply that the application has been constructed and designed very well.

Sentiment Analysis of IMDB Movie Reviews Using Recurrent Neural Network Algorithm Saputra, Aryasuta; Tobing, Fenina Adline Twince
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3610

IMDb is a well-known platform that provides user reviews and ratings of various movies. The number of reviews found on IMDb is quite large, reaching thousands of reviews. Although a movie can have a high overall rating, it is still possible to receive negative reviews from some viewers. Therefore, the purpose of this sentiment classification system is to provide a benchmark for the level of sentiment contained in the movie, and hope that filmmakers can use this information as a reference in the development of their next movie. In this research, reviews from IMDb users are classified into two types, namely positive reviews and negative reviews. The program was created using the Python language with the LSTM (Long Short-Term Memory) classification model of the RNN (Recurrent Neural Network) algorithm. The purpose of using this algorithm is to measure the level of prediction accuracy in the classification process. The results of three test ratios, namely 60:40, 70:30, and 80:20, show that in the scenario of 80% data training and 20% data testing has better performance with the results accuracy of 96%, precision of 97%, recall of 98%, f1-score of 97%.

WEB-BASED DESIGN OF DHARMA WANITA ASSOCIATION (DWP) LPKA CLASS II JAKARTA FINANCIAL REPORT APPLICATION Tobing, Fenina Adline Twince; Surbakti, Eunike Endariahna; Overbeek, Marlinda Vasty
Jurnal Sinergitas PKM & CSR Vol. 6 No. 2 (2022): OCTOBER
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/jspc.v6i2.6360

Lembaga Pembinaan Khusus Anak (LPKA)Class II Jakarta, there is a Dharma Wanita Association (DWP) Jakarta Class II LPKA organization whose members are all the wives of civil servants and also female civil servants who serve in that place. One of the keys to the success of an organization is being able to manage finances well, one of which is reflected in routinely making financial reports every month. Not only important for large companies, financial reports that are compiled and updated regularly are also important for MSME businesses, online businesses or organizations. It doesn't matter if someone doesn't have an educational background in accounting, but is still able to make complete financial reports.Technological advances encourage DWP LPKA Class II Jakarta to be able to carry out their work in terms of financial reporting to make it easier and more efficient by using applications that we have researched and implemented through the Multimedia Nusantara University (UMN) Community Service Program (PKM) in 2022 and DWP LPKA Class II Jakarta members received this form of community service with great enthusiasm. Through this PKM activity it is hoped that there will be an increase in the application of science and technology in society (mechanisms, IT, management) and for improving community values from a social and financial management perspectiveabstract in bahasaLembaga Pembinaan Khusus Anak (LPKA) Kelas II Jakarta, terdapat organisasi Dharma Wanita Persatuan (DWP) LPKA Kelas II Jakarta yang beranggotakan seluruh Istri PNS dan juga PNS wanita yang bertugas di tempat tersebut. Salah satu kunci sukses sebuah organisasi adalah mampu mengelola keuangan dengan baik, salah satunya tercermin dari rutin membuat laporan keuangan setiap bulan. Tidak hanya penting bagi perusahaan besar, laporan keuangan yang disusun dan diperbaharui secara berkala juga penting dilakukan oleh bisnis UMKM, usaha online atau organisasi. Tidak masalah jika seseorang tidak memiliki latar belakang pendidikan akuntansi, namun tetap mampu membuat laporan keuangan yang lengkap.Kemajuan Teknologi mendorong untuk membantu DWP LPKA Kelas II Jakarta untuk dapat melaksanaan pekerjaan mereka dalam hal pelaporan keuangan agar lebih mudah dan efisien dengan menggunakan aplikasi yang telah kami teliti dan kami implementasikan melalui Program Pengabdian Kepada Masyarakat (PKM) Universitas Multimedia Nusantara (UMN) tahun 2022 dan anggota DWP LPKA Kelas II Jakarta memerima bentuk pengabdian masyarakat yang kami lakukan dengan sangat antusias. Melalui kegiatan PKM ini diharapkan adanya peningkatan penerapan IPTEK di masyarakat (mekanisme, IT, manajemen) dan untuk perbaikan tata nilai masyarakat dari segi sosial dan manajeme keuangan.

Implementation of Gamification Method and Fisher-Yates Shuffle Algorithm for Design and Development Django Learning Application Kiswara, Ade; Tobing, Fenina Adline Twince; Hassolthine, Cian Ramadhona; Saputra, Muhammad Ikhwani
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3874

The web framework emerges as a solution to enhance web development efficiency. Django, an open-source web framework written in the Python programming language, is one of the popular frameworks. Currently, there are not many programming learning platforms that provide specific programming learning materials for Django, implementing a method to boost user interest in using the platform. This research aims to design and build a web-based Django learning application using gamification methods designed based on the octalysis framework to enhance user learning interest. It also incorporates the Fisher-Yates shuffle algorithm to randomize questions for more variety. The application was tested by several users by filling out a questionnaire prepared using the Hedonic Motivation System Adoption Model (HMSAM). The evaluation results of the application obtained an average percentage of 84,15% in the aspect of behavioral intention to use, which means users strongly agree that the djangoing application generates a desire to use it again in the future. Furthermore, the results in the aspect of immersion were 81,44%, which means users agree that the djangoing application creates an immersive learning experience for the Django framework.

IMPLEMENTATION OF HEURISTIC EVALUATION METHOD FOR EVALUATION AND RECOMMENDATIONS UI/UX DESIGN IMPROVEMENTS ON THE CINEPOLIS WEBSITE Aristawati, Cindy; Tobing, Fenina Adline Twince; Surbakti, Eunike Endariahna; Peranginangin, Jimmy; Pinem, Anjar
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3736

UI/UX is one of the most important elements of a website. One of the tasks of UI/UX is to make it easier to achieve a goal that the user wants. Cinépolis is a cinema that has been established in Indonesia since 2014. Cinépolis then launched its own website to make it easier for users to view movie information and order tickets. Based on the questionnaires that have been distributed and calculated using the System Usability Scale or SUS method, the Cinépolis website gets a score of 54.03 and is below the SUS standard of 68. The predicate obtained from the Cinépolis website is grade D with the predicate Poor. Heuristics are methods for finding interface problems to improve usability and user experience. The joint evaluation of 2 evaluators showed that there were 20 problems on the Cinépolis website based on 10 heuristic principles, while the evaluation of the Cinépolis website improvement prototype with 1 other evaluator found 5 problem findings based on 10 heuristic principles on the Figma prototype. The prototype that has been implemented gets a final score of 88.01 using the SUS calculation based on the questionnaire data that has been distributed. The final predicate obtained from the Cinépolis repair website is grade A with the predicate of Excellent.

Enhancing facial recognition accuracy through feature extractions and artificial neural networks Kusnadi, Adhi; Pane, Ivranza Zuhdi; Tobing, Fenina Adline Twince
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1056-1066

Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with backpropagation of 4.88%. However, the combination of DCT, GMM, and CNN feature extraction produces an accuracy of up to 98.2% and a training time of 360 seconds on the Olivetti Research Laboratory (ORL) dataset, an accuracy of 98.9% and a training time of 1210 seconds on the Yale dataset, and 100% accuracy and training time 1749 seconds on the Japanese female facial expression (JAFFE) dataset. This improvement is due to the combination of DCT, GMM, and CNN's ability to remove noise and study images accurately. This research is expected to significantly contribute to overcoming accuracy challenges and increasing the flexibility of facial recognition systems in various practical situations, as well as the potential to improve security and reliability in security and biometrics.

Enhancing Intelligent Tutoring Systems through SVM-Based Academic Performance Classification and Rule-Based Question Recommendation Tobing, Fenina Adline Twince; Haryanto, Toto
ULTIMATICS Vol 17 No 1 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i1.4178

The aims to automatically classify students' academic performance levels using Support Vector Machine (SVM) algorithm and automatically recommend questions based on classification results. Dataset consists of six assignment scores per student, averaging students into three performance levels: Beginner, Intermediate, and Advanced. Before training, the data undergoes preprocessing involving normalization with Standard Scaler and splitting into training and testing sets. The model is trained using Radial Basis Function (RBF) kernel with hyperparameter tuning to optimize its performance. The evaluation results show that the model achieved an accuracy of 91.67%, with a precision of 93.06%, a recall of 91.67%, and an F1-score of 91.89%. The best performance was found in the Intermediate class, the dominant category in the dataset, while performance in the Advanced category was relatively lower due to limited sample size. Following classification, a rule-based recommendation system is used to suggest questions that match the student's predicted level of competence. This approach supports a more adaptive and personalized learning environment. The findings demonstrate that the SVM algorithm effectively supports intelligent learning systems such as the Intelligent Tutoring System (ITS). Future work should include data balancing techniques, expansion of dataset size, and comparative analysis with other algorithms to enhance model generalization.

Implementation of Scrum Method in ERP-Based Employee Performance Evaluation System Chandra, Darren Denisson; Tobing, Fenina Adline Twince; Kusnadi, Adhi; Nainggolan, Rena; Hassolthine, Cian Ramadhona
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp201-209

Human capital is a key factor in realizing an organization’s vision and mission. To ensure optimal performance, employee output must be evaluated consistently through a well-organized appraisal process. Although PT Kompas Media Nusantara has adopted such evaluations, they are still carried out using traditional methods, such as distributing physical documents. To address these inefficiencies, an ERP-based Employee Performance Evaluation System has been designed to streamline workflows, enhance accessibility, and support a more standardized and systematic assessment process. This system utilizes Key Performance Indicators (KPIs) aligned with individual job responsibilities to measure performance. The development process adopts the Scrum methodology, while system validation is carried out through Black Box Testing. The test results reveal that the system performs reliably, achieving a 100% accuracy rate in matching inputs and expected outputs. To assess user satisfaction, the End User Computing Satisfaction (EUCS) framework combined with a Likert scale was employed. The evaluation produced high satisfaction scores across various dimensions: content (89.12%), accuracy (87.02%), layout and design (88.07%), user-friendliness (89.12%), and timeliness (86.84%). These findings indicate strong user acceptance of the ERP-based system, reinforced by consistently positive user feedback regarding its effectiveness and ease of use

Implementation of Scrum Method in ERP-Based Employee Performance Evaluation System Chandra, Darren Denisson; Tobing, Fenina Adline Twince; Kusnadi, Adhi; Nainggolan, Rena; Hassolthine, Cian Ramadhona
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp201-209

Human capital is a key factor in realizing an organization’s vision and mission. To ensure optimal performance, employee output must be evaluated consistently through a well-organized appraisal process. Although PT Kompas Media Nusantara has adopted such evaluations, they are still carried out using traditional methods, such as distributing physical documents. To address these inefficiencies, an ERP-based Employee Performance Evaluation System has been designed to streamline workflows, enhance accessibility, and support a more standardized and systematic assessment process. This system utilizes Key Performance Indicators (KPIs) aligned with individual job responsibilities to measure performance. The development process adopts the Scrum methodology, while system validation is carried out through Black Box Testing. The test results reveal that the system performs reliably, achieving a 100% accuracy rate in matching inputs and expected outputs. To assess user satisfaction, the End User Computing Satisfaction (EUCS) framework combined with a Likert scale was employed. The evaluation produced high satisfaction scores across various dimensions: content (89.12%), accuracy (87.02%), layout and design (88.07%), user-friendliness (89.12%), and timeliness (86.84%). These findings indicate strong user acceptance of the ERP-based system, reinforced by consistently positive user feedback regarding its effectiveness and ease of use

Implementation Analytical Hierarchy Process Algorithm for Design and Development Website Hero Mage Recommendation for Mobile Legends Lay, Ferry; Tobing, Fenina Adline Twince
IJNMT (International Journal of New Media Technology) Vol 10 No 2 (2023): IJNMT
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v10i2.3457

Mobile Legends is a Multiplayer Online Battle Arena genre game that is currently hot. There are 122 heroes in Mobile Legends which are divided into 6 roles. The currently popular role is mage, where this mage role occupies 3 of the 5 most used in the MPL S11 tournament. Purchasing heroes can be done with a currency called battlepoints amounting to 32,000. The collection of battlepoints is limited to one week, and there is no refund feature for hero purchases, meaning that if the player makes the wrong hero purchase, the player has to collect the currency again to be able to buy another hero. The Mobile Legends mage hero recommendation system is a system that can provide assistance in purchasing heroes that suit user preferences. Recommendation results are provided based on input provided by the user and processed using the Analytical Hierarchy Process method. The evaluation results using the End User Computing Satisfaction method obtained a percentage of 88.64%, which indicates that the system has been well developed and can be used to provide mage hero recommendations for the Mobile Legends game.

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