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DIRJA NUR ILHAM
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PERFECT: Journal of Smart Algorithms
Published by Politeknik Aceh Selatan
ISSN : 30640377     EISSN : 30640377     DOI : https://doi.org/10.62671/perfect.v1i1.1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science
PERFECT: Journal of Smart Algorithms is an international, Computer Technology, peer-reviewed and open-access journal that provides a platform to produce high-quality original research, Reviews, Letters, and case reports in natural, social, applied, formal sciences, arts, and all other related fields. Our aim is to ameliorate the speedy distribution of new research ideas and results and allow the researchers to create new knowledge, studies, and innovations that will aid as a reference tool for the future. PERFECT is published twice in one year, namely in January and July.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 29 Documents
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A Comprehensive Review on Data Science Frameworks for Big Data Analytics Raza, Hassan; Erdenetsogt, Tsendayush; Singh, A; Farooq, Mazhar; Kabeer, Muhammad Mohsin; Aslam, Muhammad Shahrukh
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.217

Abstract

The importance of big data analytics is now essential in deriving insights in large and complex information in various industries. This review discusses major data science frameworks, such as Apache Hadoop, Spark, Flink, and Storm, their architecture, capabilities, and a relative advantage of processing batches and in real-time. It also presents major challenges that can affect the framework efficiency, including scalability, latency, and heterogeneity of data, security, and the complexity of operational, among others. Lastly, the new trends such as the adoption of AI, cloud-native architecture, real-time streaming, and intelligent automation are discussed to demonstrate the changing environment. This review gives an in-depth insight into the concept of big data frameworks and how they facilitate the achievement of effective analytics.
Machine Learning Driven Decision Making in the Modern Data Era Raza, Hassan; Singh, A; Erdenetsogt, Tsendayush; Kabeer, Muhammad Mohsin; Aslam, Muhammad Shahrukh; Farooq, Mazhar
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.224

Abstract

The era of modern data has seen an unprecedented increase in the number of data generated, which generates an opportunity as well as a challenge to the decision making. Machine learning (ML) has become the significant solution to work with big and multifaceted data, recognize trends, and provide foresight and change the usual decision-making processes. This review examines the principles, methods and uses of ML-based decision systems in various industries, such as healthcare, finance, retail, transportation and education. It also analyses problems of data quality, bias, transparency, ethical considerations and developments related to explainable and trustworthy AI. Lastly, future trends, human-machine cooperation, and research perspectives are addressed, with a focus on the possibility of the ML to accelerate, more precise and answerable decisions in the world that runs on data.
Optimization of Computer Network Performance through Traffic Management and Bandwidth Allocation Ilham, Dirja Nur; Harahap, Muhammad Khoiruddin; Talib, Muhammed Saat; Budiansyah, Arie; Candra, Rudi Arif
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.243

Abstract

Computer network performance is highly dependent on effective traffic management and proper bandwidth allocation, especially in network environments with a large number of users and diverse service demands. Uneven bandwidth distribution often leads to degraded service quality, including low throughput, high delay, and increased packet loss. This study aims to analyze and optimize computer network performance through the implementation of traffic management and bandwidth allocation using the Simple Queue and Queue Tree methods on a MikroTik router. An experimental research approach was employed by comparing network performance before and after the application of bandwidth management mechanisms. The evaluation was conducted based on Quality of Service (QoS) parameters, namely throughput, delay, and packet loss, in accordance with the TIPHON standard. The experimental results indicate a significant improvement in network performance after the implementation of Simple Queue and Queue Tree. Throughput increased substantially, while delay and packet loss were considerably reduced, resulting in improved service quality categories. The Simple Queue method effectively ensured fair bandwidth distribution among users by limiting per-user bandwidth usage, whereas the Queue Tree method enhanced performance by prioritizing network traffic based on service types. The combination of these methods successfully minimized bandwidth monopolization, reduced network congestion, and improved overall network stability. Therefore, the implementation of Simple Queue and Queue Tree proves to be an effective solution for optimizing bandwidth utilization and enhancing Quality of Service in computer networks with high user density and heterogeneous traffic characteristics.
LightEmoNet: Lightweight Deep Learning for Facial Emotion Recognition Kamber, Ali Nadhim; Alkaabi, Hussein Alaa
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.273

Abstract

Facial emotion recognition (FER) is a critical component of human-computer interaction, affective computing, and intelligent surveillance systems. Existing deep learning approaches, while achieving high accuracy, are often computationally expensive and unsuitable for deployment on resource-constrained or real-time systems. In this paper, we present LightEmoNet, a lightweight Convolutional Neural Network (CNN) architecture specifically designed for efficient and accurate facial emotion recognition. Our model is trained on the FER2013 benchmark dataset, which contains 35,887 grayscale images distributed across seven emotion classes: Happy, Neutral, Sad, Fear, Angry, Surprise, and Disgust. To address the inherent class imbalance within the dataset, we employ a dual strategy combining class-weighted loss penalization with targeted data augmentation applied selectively to underrepresented categories. The proposed architecture totals approximately 2.1 million trainable parameters and occupies only 8.3 MB on disk, making it deployable on edge and embedded platforms without GPU acceleration. Experimental results demonstrate that LightEmoNet achieves a training accuracy of 91.0% and a validation accuracy of 88.5% on the FER2013 test split, with an average inference latency of 4.2 ms per image on a standard CPU. The model exhibits robust performance across all seven emotion classes while maintaining a compact footprint suitable for real-time inference. These findings confirm that lightweight CNNs, when paired with principled augmentation strategies, can achieve competitive performance without the overhead of large-scale deep models.
GPON-Based FTTH Network Design in Koto Gunung Village Iqbal Rizantha; Aprinal Adila Asril; Sahid Ridho
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.280

Abstract

The rapid development of information and communication technology has increased the demand for fast and reliable internet access, especially in rural areas. However, Koto Gunung Village, Batang Kapas District, Pesisir Selatan Regency, still experiences limited and unstable internet connectivity. Therefore, this research aims to design a Fiber To The Home (FTTH) network based on Gigabit Passive Optical Network (GPON) technology to provide stable and high-quality internet services for the community. The research method used in this study is the Network Development Life Cycle (NDLC), which includes literature study, field survey, network route design using Google Earth Pro and AutoCAD, and network simulation using OptiSystem software. The network performance was analyzed based on attenuation, link power budget, rise time budget, and Bit Error Rate (BER) parameters. The results showed that the total attenuation values for distribution route 1 and route 2 were 23.8216 dB and 23.874 dB, respectively, which are still below the maximum allowable standard of 28 dB. The total rise time values obtained were 0.2636 ns and 0.264 ns, which also meet the maximum standard of 0.29 ns. In addition, the simulation results showed BER values of 1.42 × 10⁻¹⁹ and 2.322 × 10⁻¹⁹, indicating excellent transmission quality with very low error rates. Based on these results, the designed GPON-based FTTH network is considered feasible for implementation in Koto Gunung Village.
Impact of Industrial Noise on Production Efficiency Osamah Ibrahim Ali Barka; Abdulqadir M. Alhadar; Musbag Ahedery; Omer I. A. Hmellah; Nuri Salem Ali Abosetha; Ahmed Alnagrat
PERFECT: Journal of Smart Algorithms Vol. 3 No. 2 (2026): PERFECT: Journal of Smart Algorithms, Article Research July 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i2.281

Abstract

Industrial noise is a common problem in manufacturing environments because it affects occupational comfort, machine reliability, and production efficiency. Objective: This study examines whether acoustic monitoring and sound pressure level measurement can be used to connect industrial machinery noise with output losses and maintenance needs. Methods: The method combined machine observation, acoustic signal recording, cutter cycle timing, production counting, and comparative analysis of a pasta packaging machine, lathe, multi-spindle drilling machine, and cigarette production machine. Results: The pasta packaging machine showed the clearest relationship between noise behavior and productivity. Its cutter was designed to operate every one and one-half seconds, but the measured acoustic rhythm indicated an average cycle of about one and two-thirds seconds. This timing difference reduced output from four hundred to three hundred sixty-one bags in ten minutes, equal to a loss of about ten percent. Other machines with higher sound pressure levels also showed signs of wear, vibration, and reduced operational stability. Conclusion: Industrial machinery noise is not only a workplace hazard but also a practical indicator of machine condition. The study supports using noise control, maintenance, acoustic monitoring, industrial machinery assessment, and production efficiency evaluation as an integrated approach for improving reliability and manufacturing performance.
ITAF Kupang New Student Admission Prediction Using The Random Forest Method Mohamad Iqbal Ulumando; Orry Adrianus Mokola
PERFECT: Journal of Smart Algorithms Vol. 3 No. 2 (2026): PERFECT: Journal of Smart Algorithms, Article Research July 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i2.284

Abstract

New student admission is a crucial aspect of higher education academic planning. The Alberth Foenay Institute of Technology (ITAF) Kupang requires a data-driven approach to predict the number of new students in each study program to support more accurate decision-making. This study aims to predict the number of new student admissions at ITAF Kupang in the 2026/2027 academic year using the Random Forest method. The data used comes from historical data on new student admissions over the past five years (2021–2025) in three study programs: Informatics, Environmental Engineering, and Mechanical Engineering. The year and study program variables are used as input variables, while the number of new students is used as the output variable. The research stages include data pre-processing, transformation and encoding of categorical variables, Random Forest modeling, and model evaluation using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The model evaluation results show an MAE value of 9.11 and an RMSE of 10.58, indicating that the model has quite good predictive performance. The prediction results show that the number of new students in the 2026/2027 academic year is estimated to be 41 students for the Informatics Study Program, 24 students for Environmental Engineering, and 16 students for Mechanical Engineering. This research is expected to be a supporting basis for planning new student admissions at ITAF Kupang.
Prediction Of Repeating Object-Oriented Programming Course for Informatics Students at ITAF Kupang Using Extreme Gradient Boosting (XGBoost) Mohamad Iqbal Ulumando
PERFECT: Journal of Smart Algorithms Vol. 3 No. 2 (2026): PERFECT: Journal of Smart Algorithms, Article Research July 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i2.285

Abstract

The Object-Oriented Programming course is one of the core courses in the Informatics Study Program which has a fairly high level of difficulty so that some students have the potential to fail and have to repeat the course. This study aims to build a prediction model for students of the Informatics Study Program at ITAF Kupang who have the potential to repeat the Object-Oriented Programming course using the Extreme Gradient Boosting (XGBoost) algorithm based on student learning behavior data. The data used amounted to 60 students with variables including attendance, assignment grades, accuracy of assignment submission, discussion participation, quiz scores, practicum activities, and mid-term/final exam scores. The research stages include data collection, data preprocessing, training and testing data distribution, XGBoost model training, and model evaluation using Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results of the study showed that the XGBoost model was able to perform good classification with an Accuracy value of 83.33%, Precision of 80.00%, Recall of 80.00%, and F1-Score of 80.00%. Feature importance analysis showed that quiz scores were the most influential factor in students' potential to repeat courses, followed by mid-term/final exam scores and assignment scores. The results of the study proved that student learning behavior data can be used to build an early warning system that helps lecturers and study programs identify at-risk students early on so that more effective academic mentoring can be provided.
Application Of The COPRAS Method In Selecting The Best Boarding House For Students Chairina Sakinah Sitorus; Nanda Afira
PERFECT: Journal of Smart Algorithms Vol. 3 No. 2 (2026): PERFECT: Journal of Smart Algorithms, Article Research July 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i2.288

Abstract

Selecting a boarding house is an important decision for students, especially those who come from outside the area and require a comfortable, safe, and suitable place to live. The large number of boarding house options with varying facilities and rental prices often makes it difficult for students to determine the best choice. Therefore, a decision support system is needed to assist the selection process objectively and effectively. This study aims to implement the Complex Proportional Assessment (COPRAS) method in selecting the best boarding house for students based on several predetermined criteria. The criteria used in this study include rental price, distance to campus, facilities, and security. The COPRAS method was chosen because it is capable of evaluating both benefit and cost criteria proportionally. The results of the study indicate that the COPRAS method can rank boarding house alternatives based on their priority values. Based on the calculation results, Alternative A1 (Kost M & U) was identified as the best boarding house with the highest priority value compared to the other alternatives. Therefore, the implementation of the COPRAS method can help students determine boarding house choices that best match their needs and preferences in a faster, more accurate, and objective manner

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