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INTI Nusa Mandiri
Published by PPPM Nusa Mandiri
ISSN : 02166933     EISSN : 2685807X     DOI : -
Core Subject : Science,
Computer Science & IT
The INTI Nusa Mandiri Journal is intended as a media for scientific studies on the results of research, thought and analysis-critical studies on the issues of Computer Science, Information Systems and Information Technology, both nationally and internationally. The scientific article in question is in the form of theoretical review and empirical studies of related sciences, which can be accounted for and disseminated nationally and internationally.
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Articles 234 Documents
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ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM) Dwi Tiyas Novitasari; Barata, Mula Agung; Yuwita, Pelangi Eka
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6297

Abstract

The Originote Hyalucera Moisturizer skincare product has attracted public attention because it offers superior quality at an affordable price. Social media, especially Twitter, is used by consumers to express opinions regarding this product, whether positive, negative, or neutral. However, the large number of reviews with various sentiments can confuse potential consumers in assessing product quality. Therefore, this study aims to understand user perception through sentiment analysis and evaluate the effectiveness of the Support Vector Machine (SVM) algorithm in sentiment classification. A total of 1,820 tweets were collected using the crawling technique with Python. The data undergoes preprocessing, including text cleaning, tokenization, stopword removal, and stemming, reducing it to 902 tweets. Key text features are extracted using Term Frequency-Inverse Document Frequency (TF-IDF). For sentiment classification, this study used the SVM algorithm, which is known as an effective method in text processing. Model evaluation showed good results with an accuracy of 87%, precision of 89%, and recall of 87%. This study provides insight into public perception of The Originote Hyalucera Moisturizer and measures the effectiveness of SVM in social media-based sentiment analysis. The results of the study can be utilized by manufacturers for more targeted marketing strategies, product quality improvement, and more effective communication in responding to opinions on social media. In addition, this study contributes to the development of machine learning-based sentiment analysis methods in the context of skincare products.
IMPLEMENTASI MODEL DeiT UNTUK MEMBEDAKAN GAMBAR BUATAN AI DAN MANUSIA PADA ILUSTRASI ANIMASI 2D Erwin, Ibnu Taimiyah; Abdul Latief Arda; Imran Taufik; Muhammad Erwin Rosyadi. S; Hilyatul Auliyah Erwin
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6306

Abstract

The development of artificial intelligence (AI) has influenced various fields, including art and visual design. AI Generative Art, which mimics human styles, has sparked debates on originality, artistic value, as well as legal and ethical challenges. Therefore, methods are needed to distinguish between AI-generated and human-made images, particularly in 2D animation illustrations. This study proposes the use of Data-efficient Image Transformers (DeiT) for image classification. Two models tested are DeiT Base and DeiT Tiny, using a dataset of 6,000 images equally divided between AI and human categories. The dataset is split into training (70%), validation (15%), and testing (15%). Experimental results show that DeiT Base achieves over 95% accuracy with fast convergence and optimal loss function stability. Meanwhile, DeiT Tiny attains around 93% accuracy, being more computationally efficient despite requiring more epochs for stability. Compared to previous models using a larger dataset (11,000 images per category) but achieving only 80% accuracy, DeiT performs better in both accuracy and computational efficiency, even with a smaller dataset. In conclusion, DeiT is effective for classifying 2D animation images. DeiT Base excels in accuracy and convergence speed, while DeiT Tiny is more resource-efficient, making it an ideal choice for environments with computational constraints.
PERANCANGAN SISTEM INFORMASI WEBSITE PROFILE SEKOLAH SEBAGAI SARANA PROMOSI Sari, Ani Oktarini; Kholil, Ishak
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6310

Abstract

The development of information technology encourages educational institutions to utilize digital media in supporting promotional activities and information dissemination. This research aims to design a website-based school profile information system that functions as a promotional medium and increases the dissemination of information and expands the reach of school promotion to the community, especially prospective students and parents. This information system is designed to increase transparency, accessibility, and effectiveness in conveying school information to the public. This information system presents information about school profiles, school activities, school facilities, and school contacts in an interactive and structured manner. The Waterfall method is used in system development, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. In the needs analysis stage, the main information required is identified through interviews and surveys. Then, the interface design was made with attention to the aspects of user-friendliness and responsiveness for various devices. The implementation phase uses the codeigniter framework to build a dynamic and easy-to-manage system. Testing is carried out using the blackbox testing method to ensure that the system functionality runs according to specifications. The results of the study show that this website-based school profile information system is able to present information effectively, increase school visibility, and facilitate interaction with prospective students and parents. This system can also provide added value in building the school's image in the digital era.
ANALISIS DISTRIBUSI MINAT MAHASISWA PADA KONSENTRASI INFORMATIKA MENGGUNAKAN PENDEKATAN DATA-DRIVEN DECISION MAKING Mahardika, Fathoni; Supriadi, Fidi; Guntara, Agun
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6347

Abstract

In the digital era, higher education institutions face the challenge of aligning the curriculum with the dynamic demands of the industry. This research aims to identify patterns of student interest in choosing specialization concentrations in the Informatics Study Program (S1), Universitas Sebelas April, using a data-driven decision-making approach. The study involved 133 5th semester students out of a total population of 500 students in the Computer Science program at Sebelas April University. The respondents were selected because they were at the relevant stage of study to determine the specialization concentration, the results of which provide important recommendations for curriculum optimization and resource allocation.Student specialization survey data were analyzed using descriptive statistics, data visualization, and trend analysis to provide data-driven insights to support more efficient academic planning. The results showed that the concentration of "Computer Science, Software, and Intelligent Systems" was more desirable than "System Security and Computer Networks".
PENERAPAN POLA FIBONACCI UNTUK PENGATURAN QOS (QUALITY OF SERVICE) JARINGAN Gani, Ahmad; Wibawa, Sigit; Ilyas , Fadli
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6359

Abstract

In managing network quality of service (QoS), this research uses the Fibonacci pattern to optimize delay control and bandwidth allocation. QoS is very important in contemporary network management, especially considering the increasing demand for stable and effective data services. This study prioritizes data based on traffic levels using a Fibonacci algorithm simulation. Each priority is assigned a value corresponding to the Fibonacci sequence, which allows for resource allocation that is more in line with network load.The simulation was conducted under normal and overload conditions. The research results show that conventional methods, such as round-robin and weighted fair queuing, can improve QoS efficiency with the Fibonacci pattern by up to 15%. This improvement primarily focuses on managing important data packets such as real-time communication and video streaming, and reducing latency. Additionally, this technique is better at adapting to traffic changes.The research results show that the Fibonacci pattern can be an innovative method for managing network QoS, especially for complex priority needs. By using the Fibonacci pattern as a data priority management technique, this research helps improve network quality of service (QoS). This method is capable of improving bandwidth allocation efficiency and reducing latency by up to 15% compared to conventional approaches such as Round-Robin and Weighted Fair Queuing. The main contribution of this research is to offer a new approach based on Fibonacci patterns that can be adapted to the dynamics of network traffic.
PENERAPAN DECISION TREE DENGAN PENYEIMBANGAN DATA IMBALANCE MENGGUNAKAN UPSAMPLING DALAM PREDIKSI PENYAKIT LIVER Agung Fazriansyah; Yuris Alkhalifi; Ainun Zumarniansyah
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6369

Abstract

Acute liver disease has a significant impact on liver function and is often only detected at an advanced stage due to the lack of patient awareness for early examination.  One of the challenges in treating liver disease is the delay in diagnosis, where many patients do not notice the early symptoms until their condition has worsened.  Therefore, a predictive system is needed that can identify liver disease patients early on, allowing for regular check-ups and timely treatment.  In this study, a classification model was developed using a machine learning approach, specifically the Decision Tree algorithm, by balancing the data in the minority class through upsampling.  The research results show that this model is capable of predicting liver disease status with an accuracy rate of 89.22%, a recall of 88.45%, a precision of 83.21%, and an f1-score of 85.78%.  In addition, the ROC-AUC value of 0.89 is categorized as a good classification.  This model achieved a higher accuracy score than other studies with similar datasets.  This system is expected to help improve early detection and expedite the treatment of liver disease patients.
OPTIMASI HYBRID INTELLIGENT SYSTEM UNTUK IDENTIFIKASI BUAH: STUDI KASUS PISANG DAN APEL Yanti, Rahma; Agung Ramadhanu
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6382

Abstract

Image processing-based fruit classification is one of the rapidly developing technology applications in the field of digital agriculture. This study aims to develop a fruit identification system, especially yellow bananas, green bananas, and apples, by utilizing the K-Nearest Neighbors (KNN) and Principal Component Analysis (PCA) methods. The background of this study is the need for an accurate automatic system to distinguish fruit types based on visual characteristics, such as color, texture, and shape, to support the distribution and management of agricultural products. The method used in this study involves four main stages: image loading, segmentation, feature extraction, and classification. PCA is used to reduce data dimensions by maintaining relevant main features, while KNN functions for classification based on the closest distance between test data and training data. The dataset used consists of 130 images, with 120 images as training data and 10 images as test data. The results of the study show that the developed system is able to classify all test data with 90% accuracy. This success proves that the combination of PCA and KNN methods is effective in identifying fruit types based on extracted visual characteristics. This system is expected to be the basis for further development in the field of automatic fruit classification.
PENGEMBANGAN SISTEM AUDIT TERINTEGRASI PADA SIAKAD MENGGUNAKAN FRAMEWORK COBIT 5 Kurniawan, Andri; Situmeang, Nur Ansori Hamidah; Falgenti, Kursehi
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6389

Abstract

The Academic Information System (SIAKAD) plays a crucial role in supporting academic activities within the university environment. Evaluating the system's maturity is necessary to identify weaknesses and potential improvements. Therefore, periodic audits of SIAKAD are essential to obtain constructive feedback from stakeholders. Conducting regular audits of SIAKAD is more effective when supported by an application integrated with the system. This study aims to develop an audit system integrated with SIAKAD. The COBIT 5 framework is used for auditing SIAKAD, while the audit system is developed using PHP programming language and a MySQL database. The focus of this research is the development of an audit system within the Evaluate, Direct, and Monitor (EDM) domain. This study proposes a new approach to developing an effective SIAKAD audit system by integrating SIAKAD with a COBIT 5-based audit system. The developed audit system has been tested on a limited scale with 20 respondents from the Bantaeng Manufacturing Industry Community Academy. At this stage, the audit system development has only reached the process of summarizing questionnaire results. The calculation of SIAKAD maturity levels has not yet been integrated and is still performed manually. Gap analysis results indicate that SIAKAD’s maturity level remains low, with the following scores:EDM1: 30, EDM2: 20, EDM3: 33.6, EDM4: 16.7, and EDM5: 23.3. Based on these maturity scores, improvements in governance are required, particularly in the EDM2 and EDM4 subdomains.
SISTEM DETEKSI GEMPA BERBASIS IOT DENGAN VISUALISASI REAL-TIME DAN NOTIFIKASI CERDAS Dinata, Riadi Marta; Ariman, Ariman; Yamin, Muhammad Ikrar
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6394

Abstract

Indonesia, as a region with high seismic activity, requires a fast, accurate, and reliable disaster mitigation system. However, most existing earthquake detection systems still focus primarily on data collection without automatic notifications, which delays response times in emergency situations. This study develops an Internet of Things (IoT)-based early earthquake detection system that integrates a gyroscope sensor, the ThingSpeak cloud platform, and an Android application to provide real-time information to users. The system detects orientation changes along the X, Y, and Z axes, calculates vibration magnitude through a calibrated algorithm, and sends automatic notifications via WhatsApp to mitigation officers. Testing was conducted through simulations using Wokwi to validate the algorithm and physical implementation in real-world conditions, demonstrating that the system achieves high accuracy in detecting seismic activity, with an average accelerometer magnitude of 3.35 and a gyroscope magnitude of 4.19. Data visualization on ThingSpeak, along with graphical displays in the Android application, enables intuitive and real-time earthquake monitoring. The integration of smart notifications via WhatsApp ensures a fast response from mitigation officers, making it an effective and applicable solution for earthquake risk mitigation.
IMPLEMENTASI HYBRID INTELLIGENCE SYSTEM UNTUK KLASIFIKASI BIJI-BIJIAN DENGAN ALGORITMA PCA DAN KNN Chan, Fajri Rinaldi; Ramadhanu, Agung
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6397

Abstract

Food security has become a pressing global issue with the increasing population and food consumption needs. Red kidney beans, peanuts, and sunflower seeds play a crucial role in meeting the nutritional needs of society and serving as raw materials for various industries. This study aims to develop a seed classification system based on the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms. The system is designed to recognize three types of seeds—red kidney beans, peanuts, and sunflower seeds—to improve the efficiency and accuracy of the classification process compared to manual methods. The dataset consists of 58 seed image samples, divided into training data (48 samples) and test data (10 samples). The research stages include image preprocessing (cropping, background removal, and thresholding segmentation), feature extraction using PCA to reduce data dimensionality, and classification with KNN based on Euclidean distance. A value of K=3 is used in the KNN algorithm to determine the proximity between data points. The test results show a classification accuracy of 90%, with 9 out of 10 test data correctly classified. PCA successfully simplified high-dimensional data into two main components without significant information loss, while KNN demonstrated strong capability in distinguishing the three types of seeds. This research contributes to the development of an AI-based automatic classification system for the food industry, with broader potential applications in high-dimensional data processing across various fields.

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