Jurnal Ilmiah Teknik Informatika (TEKINFO)

Efisiensi Pendataran Memori pada Komputasi Matriks Padat: Studi Komparatif Rust dan Go

Hamdan Yuwafi Mastu Wijaya — 2026-07-10

Modern high-performance computing (HPC) demands face the Memory Wall, where
memory layout efficiency is now more crucial in determining performance than the number
of processor cores. Nested dynamic array structures inherently induce pointer chasing,
which breaks hardware-level data locality. This paper presents an architectural comparative
study between the concurrency models of Go (M:N scheduling) and Rust (1:1 kernel
threads) through the evaluation of intensive matrix computations on the order of 500x500
to 2000x2000. Experiments contrast conventional nested memory structures against a flat
linear layout (1D Contiguous Memory Layout). Empirical results from isolated tests show
that Go's native implementation dominates in efficiency at medium scales due to its low
Goroutine initiation overhead. However, in Rust, nested structures induce exponential
latency spikes due to high cache miss metrics. Memory flattening in Rust has been shown
journals.upi-yai.ac.id/index.php/TEKINFO/issue/archive
P-ISSN: 1411-3635
E-ISSN: 2962-5645
TEKINFO VOL. 27, NO. 2, Oktober 2026 1
to eliminate pointer chasing, reduce computational latency by up to 32.3% at the highest
order, and trigger the activation of automatic vectorization (SIMD) instructions by the
LLVM compiler. In contrast, manual application of linear index transformations in Go
actually creates a massive performance degradation of 79% due to the accumulated
arithmetic calculation load that the runtime fails to optimize. This research demonstrates
that there is no absolute language superiority; architecture selection should be based on the
alignment of Data-Oriented Design with compiler capabilities, providing concrete
recommendation parameters for designing industrial-scale software systems.

Deteksi Tingkat Kematangan Buah Pisang Berdasarkan Citra Digital Menggunakan Metode Convolutional Neural Network (CNN)

Faidatul Hanafiyah — 2026-07-10

color of the fruit's skin. This method often produces different assessment results due
to the influence of subjective human factors. This research aims to create a system that
can recognize banana ripeness using digital images using a convolutional neural
network (CNN) method. Data were collected by photographing bananas using a mobile
phone camera, and the bananas were categorized into four categories: unripe, ripe,
overripe, and rotten. The research process consisted of several steps: data collection,
data labeling, image processing by resizing the images to 224x224 pixels, normalizing
pixel values, converting the color format to RGB, data augmentation, training the CNN
model, and evaluating model performance. The CNN architecture used consisted of
three convolutional layers, three max-pooling layers, a flatten layer, a dropout layer, and
a dense layer. Tests demonstrated the model's ability to classify banana ripeness with up to 96% accuracy. Good precision, recall, and F1-score values indicate the CNN
method's success in recognizing banana ripeness from digital images.

RANCANG BANGUN SISTEM ANALISIS NILAI SISWA SDN 13 PAGI DUREN SAWIT BERBASIS ETL DAN OLAP

Agus Wismo Widodo — 2026-07-10

Rapid information technology growth prompts the integration of information systems for managing
student grades as a basis for learning evaluation. Currently, SDN 13 Pagi Duren Sawit manages these
grades manually, separating the odd and even semesters, which hinders teachers from performing
systematic analysis and comparisons. This study designs an analytical system using Extract, Transform,
Load (ETL) and Online Analytical Processing (OLAP) to integrate and structure student grade
management. Utilizing an applied research method with the Waterfall SDLC approach, the ETL process
ensures data consistency, while OLAP enables multidimensional analysis by student, subject, and
semester. The web-based system was verified through Black Box Testing and evaluated using the System
Usability Scale (SUS)

Rancang Bangun Game Edukasi Santri Quest Berbasis Unity 3D Sebagai Media Pembelajaran Interaktif Bagi Santri

Widya Safitri — 2026-07-10

The development of digital technology has significantly influenced various sectors,
including education. One form of technology utilization in education is the implementation
of educational games as interactive learning media. Educational games can improve
learning motivation by combining entertainment and educational elements into a single
platform. Salafiyah Syafiiyah Sukorejo Islamic Boarding School has various regulations
that must be understood by students. However, the delivery of these regulations is still
mostly conducted through conventional methods, making it less attractive for today's
digital generation. This study aims to design and develop a Unity 3D-based educational game called Santri
Quest as an interactive learning medium for students. The research method used is Research
and Development (R&D), while the system development method adopts Rapid Application
Development (RAD). The game was developed using Unity Engine and C# programming
language. In this game, players act as students who must overcome obstacles, avoid
hazards, and answer educational quizzes related to Islamic jurisprudence and science to
reach the Freedom Finish as the final objective.
The results show that the game has been successfully implemented with features such as
Loading Screen, Main Menu, Select Quiz, Select Hero, Hero Controller, hero characters,
monster characters, and quiz-based educational gameplay. Based on Black Box Testing, all
major features function according to system requirements. Santri Quest is expected to
become an attractive learning medium that enhances studentsâ€™ understanding of
educational materials and boarding school regulations

Sistem Pendukung Keputusan Pemilihan Program Studi Mahasiswa Baru Menggunakan Metode SAW Di Universitas Ibrahimy

Siti Maulidatul Hasanah — 2026-07-10

The selection of a study program is a strategic step for prospective new students, as it
influences long-term academic success and career outcomes. However, many prospective
students find it difficult to choose a study program that aligns with their interests and
abilities, which can reduce their motivation to learn and increase the likelihood of dropping
out. This study aims to develop a web-based decision support system capable of providing
objective study program recommendations using the Simple Additive Weighting (SAW)
approach at Ibrahimy University Sukorejo Situbondo. The SAW method was chosen
because it is simple, efficient, and capable of ranking alternatives based on predetermined
criteria weights. The criteria used include Academic Potential Test (TPA) with a weight of
20%, General Knowledge Test (TPU) 20%, Arabic Language 10%, English Language
10%, Study Program Competency Test 30%, and Psychology Test 10%. The system
development follows the waterfall model, including the stages of requirements analysis, system design, implementation using PHP and MySQL, testing, and maintenance. The
results show that the developed system is able to perform matrix normalization, calculate
the preference score (Vi) for each study program alternative, and generate accurate and
efficient recommendation rankings. This system makes it easier for prospective new
students to choose a study program that matches their potential, while also assisting
Ibrahimy University in carrying out a more structured, transparent, and measurable
selection process.

Perbandingan Kinerja YOLOv8 dan SSD MobileNet untuk Deteksi Kelengkapan Atribut Seragam Siswa

Argi Ginanjar — 2026-07-10

Inspecting the completeness of student uniform attributes is challenging because several items, such
as epaulettes, name tags, belts, black shoes, and caps, may appear small, partially occluded, or
visually unclear. This study compares YOLOv8n and SSD MobileNet for detecting student uniform
attributes using a self-collected dataset. The dataset contains 1,532 augmented images, five object
classes, and 5,294 YOLO-format bounding box annotations. The experimental procedure included
dataset preparation, object annotation, train-validation-test splitting, model training, and final
evaluation on the test set. The evaluation used precision, recall, mAP@50, mAP@50-95, inference
time, and FPS. On the test set, YOLOv8n achieved a precision of 0.8966, recall of 0.8689, mAP@50
of 0.9276, mAP@50-95 of 0.6723, inference time of 10.36 ms, and 96.47 FPS. SSD MobileNet
achieved a precision of 0.5140, recall of 0.5169, mAP@50 of 0.6034, mAP@50-95 of 0.3171,
inference time of 11.00 ms, and 90.87 FPS. These findings indicate that YOLOv8n performed better
in the tested dataset and experimental setting.

MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA APRIORI UNTUK STRATEGI PENJUALAN PRODUK BAHAN BANGUNAN DI CV ALBA

Husein Zidan — 2026-07-10

This research is motivated by the decline in sales of HMD brand Concrete Roof tiles and the lack of
digitalization systems at CV ALBA during the period of 2024–2025. The decrease in sales indicates a
reduction in consumer purchasing interest toward HMD concrete roof tile products. Based on the results of
observations, the researcher applies Market Basket Analysis to address the purchasing decline in building
material products, and incorporates the concept of Quantitative Association Rules, as the dataset consists not
only of per-item product transactions but also includes purchase quantities integrated into the model. The
application of Market Basket Analysis is expected to provide solutions to the identified problem. The data
analyzed in this study comprises 1.000 transactions along with their purchase quantities, collected from
January 2024 to June 2025. The Apriori algorithm was applied using minimum support of 3%, minimum
confidence of 30%, and lift ratio > 1, resulting in 6.421 generated rules. From all the rules formed, the
researcher selected the top 3 best rules from 2-itemset to 7-itemset as key insights for product bundling sales
recommendations.