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Tematik : Jurnal Teknologi Informasi Komunikasi

tematik
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Published by POLITEKNIK LP3I BANDUNG

ISSN : 23559055 EISSN : 24433640 DOI : 10.38204

Core Subject : Science,

Computer Science & IT

TEMATIK - Jurnal Teknologi Informasi Dan Komunikasi merupakan jurnal ilmiah sebagai bentuk pengabdian dalam hal pengembangan bidang Teknologi Informasi Dan Komunikasi serta bidang terkait lainnya. TEMATIK - Jurnal Teknologi Informasi Dan Komunikasi diterbitkan oleh LPPM dan Program Studi Manajemen Informatika di Politeknik LP3I Bandung. Redaksi mengundang para dosen, peneliti dan professional dari dunia industri dan kerja untuk menulis karya ilmiah dan pengalaman praktis di lapangan terkait implementasi Informatika dan Komputer.

Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi

Articles 252 Documents

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Prediksi Harga Material Bangunan Dengan Autoregressive Integrated Moving Average (Arima) Pada CV. TJA Christina Purnama Yanti; Ni Komang Ita Cahyani; Theresia Hendrawati; Yuri Prima Fittryani; Dewa Ayu Kadek Pramita
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1914

The problem most often faced by CV TJA is that the price from the RAB (Cost Budget Plan) calculation is not in accordance with market prices, so it is necessary to predict the price of building materials to help the company prepare the RAB. The aim of this research is to examine predictions of material prices measured using the ARIMA method. The data analysis method in this study used the MA parameter (1) with the ARIMA model (0,1,1) for dynamix cement material, the MA parameter (1) ARIMA model (0,2,1) for cast sand, the MA parameter (1 ) ARIMA model (0,3,1) for threaded iron 13 and AR parameter (2) ARIMA model (2,1,0) for usuk wood material. By using 3 error testing methods, namely Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Deviation (MSD). The results of this analysis show the lowest accuracy value, namely for dynamic cement material with a MAPE value of 5%, a MAD value of 1.986 and an MSD value is 63.584.667. The results of error tests using MAPE, MAD and MSD can be concluded that the ARIMA method is very accurate because the MAPE value is less than 10%.

Sistem Pakar Diagnosis Penyakit ISPA Pada Anak Tasya Mutiara Diva; Joni Maulindar; Sri Sumarlinda
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1916

Acute Respiratory Tract Infections (ARTIs) in children are often challenging to diagnose due to their similar and varied symptoms. Hence, the development of an expert system for diagnosing ARTIs in children is crucial. The aim of this research is to develop an expert system that can assist in the rapid and accurate diagnosis of ARTIs in children. The method employed is the forward chaining approach, where observed symptoms are linked to specific ARTIs through inference rules. Initially, the symptoms of ARTIs and related diseases are identified, forming the knowledge base. Subsequently, the forward chaining algorithm is implemented in the system to facilitate the dissemination of information from observed symptoms to the correct diagnosis. The user interface is designed to facilitate input from users, whether doctors or parents, regarding their child's symptoms. Internal testing is conducted to validate the system's accuracy, involving medical professionals to evaluate and provide feedback on the generated diagnoses. The results indicate that bronchitis is the most likely disease in children based on the total scores of observed symptoms. Treatment recommendations focus on symptomatic care and complication prevention. Thus, the use of an expert system with a forward chaining approach positively contributes to supporting the diagnosis of ARTIs in children by providing faster and more accurate diagnoses and appropriate treatment recommendations.

Perancangan Prototype Tirai Hujan Otomatis Berbasis Arduino Nabil Radityo B.P; Kinanti Ayu Hasanah; Darryl Endra Zachary; Adli Ahmad Hazimulfikri; Akila Shina Kusumaningrum; Aliya Rusyda Muthmainah
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1919

Sensors are an important element in the automation process. The automation process itself is one of the important concepts in smart living, which allows us to live more comfortably with the help of certain technological devices, which generally use sensors. The combination of sensors with microcontrollers can help us do a job automatically. One of them is to close the curtains automatically in case of rain. This process can be done by connecting a sensor to detect rainwater or a raindrop sensor, with an Arduino microcontroller, and a motor to move the curtain. This research aims to design an automatic rain curtain prototype using a raindrop sensor as a rainwater detector. This prototype is built using MH-RD raindrop sensor, Arduino Uno R3 and 995 180o servo motor. This device is then simulated on a model in the form of a mini house mockup equipped with a rain protection curtain for clothespins and tested by spraying 5ml of water using a mini sprayer 3 times. The simulation results show that the curtain can close automatically when the sensor is splashed with water, and reopen when the sensor does not detect water.

Penerapan Metode TOPSIS Terhadap Skala Prioritas Aspirasi Civitas Kampus UKRI Novianti Indah Putri; Yogi Saputra; Faisal Aziz; Sigit Sugara; Kaka Sultan M Guteres
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1921

The campus community at the National University of the Republic of Indonesia has a fairly high level of criticism of the development of the campus itself, however, it turns out that the campus does not have sufficient media to accommodate the aspirations expressed by the campus community. Aspirations can be conveyed, but with a limited scale and time that also makes it difficult for campuses to make decisions that will later have an impact on the development of the National University of the Republic of Indonesia campus, therefore the TOPSIS method is here to make it easier for campuses to make decisions. The TOPSIS calculation refers to the alternative distances to the positive ideal solution and the negative ideal solution. From the results of calculations that have been normalized, the highest value has the right to get priority as the best solution, and conversely, the lowest value is not prioritized to be used as a reference in decision making.

Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara Pramudita, Rully; Muis, Saludin; Safitri, Nadya; Shafirawati, Fitri
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1928

Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of classification algorithms in Machine Learning. Classification algorithms that will be evaluated include Support Vector Machine (SVM), Neural Network, Logistic Regression, Decision Tree, and K-Nearest Neighbors (K-NN). Evaluation of the performance of these algorithms will be carried out using the confusion matrix, Receiver Operating Characteristic (ROC) Curve, and Area Under Curva (AUC). The result of this research is a comparative analysis of the optimization of classification algorithms using the bagging technique. After carrying out the evaluation process using the confusion matrix and ROC curve, it was found that the algorithm optimization using the bagging technique only had an effect on the Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms. . The accuracy of the DT algorithm increased by 0.6% while the accuracy of KNN increased by 1.3%. The AUC value for the DT algorithm increased by 1.4% and the KNN algorithm increased by 0.3%.

Analisis Sentimen Twitter Terpilihnya Prabowo - Gibran Menggunakan Metode Neural Network Diana Dwi Rahayu; Muhammad Fatchan; Alfonsus Ligouri
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1943

One of the most important elections in Indonesian democracy is the presidential election, which chooses the country's leader for the next five years. In the 2024 presidential election, there are three candidates for president and vice president, including the Prabowo Subianto - Gibran Rakabuming Raka pair. The election process has taken centre stage on social media, particularly Twitter, where people interact, share information, and express their opinions and feelings. This study aims to look at public opinion towards the Prabowo-Gibran team, which has attracted a lot of attention since Gibran was nominated as a vice presidential candidate until he was declared the winner in the 2024 presidential election by the KPU. This analysis provides valuable insight into understanding public opinion and feelings towards the president and vice president-elect. The method used in this research is neural network (NN), which is proven to be effective in text data classification and capable of producing high accuracy. The dataset used is public opinion on Twitter, which is taken through the data crawling process. The initial data of 1511 tweets was then cleaned and prepared into a dataset of 1500 tweets, with the main attribute being the content of the tweet. Based on the findings, the neural network model created was able to classify the sentiment of tweets related to the Prabowo-Gibran pair with an accuracy rate of 93%. Thus, this sentiment analysis makes an important contribution to understanding the public's response to the presidential election process and the election of a new president and vice president

Exploration of Acceptance Factors of Online Learning Platform: A Theory of Planned Behavior Perspective Adi Dharma Putra; Addin Aditya; Arif Tirtana
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2064

E-learning is recognized as a form of education facilitated through the application of information and communication technologies. The successful implementation of e-learning is influenced by numerous factors. This research aims to identify the determinants of online learning platform acceptance through the lens of the Theory of Planned Behavior (TPB). The study focuses on three independent variables: attitudes, subjective norms, and perceived behavioral control. Positioned as explanatory research with a quantitative emphasis, data were gathered from active students who met specific criteria and were subsequently given access to an online questionnaire. The research sample was obtained through a purposive sampling method. This study employs multiple linear regression analysis, executed through computational means. The hypothesis testing results indicate that two hypotheses—subjective norms on intention and intention on behavior—have a positive and significant influence, suggesting that perceptions from one's social environment can significantly shape users' intentions to engage with online learning platforms. However, the remaining three hypotheses reveal a positive yet insignificant effect, indicating that the variables of behavior and attitude control do not significantly impact the use of online learning platforms. Collectively, the four variables account for only 43.7% of the behavioral outcomes, with the remaining 56.3% likely influenced by other factors beyond the scope of this research. E-learning dikenal sebagai bentuk pendidikan yang difasilitasi melalui penerapan teknologi informasi dan komunikasi. Keberhasilan implementasi e-learning dipengaruhi oleh banyak faktor. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor penentu penerimaan platform pembelajaran online melalui lensa Theory of Planned Behavior (TPB). Penelitian ini berfokus pada tiga variabel independen: sikap, norma subjektif, dan kontrol perilaku yang dirasakan. Diposisikan sebagai penelitian eksplanatori dengan penekanan kuantitatif, data dikumpulkan dari mahasiswa aktif yang memenuhi kriteria tertentu dan kemudian diberikan akses ke kuesioner online. Sampel penelitian diperoleh melalui metode purposive sampling. Penelitian ini menggunakan analisis regresi linier berganda, yang dilakukan dengan cara komputasi. Hasil pengujian hipotesis menunjukkan bahwa dua hipotesis - norma subjektif terhadap niat dan niat terhadap perilaku - memiliki pengaruh yang positif dan signifikan, yang menunjukkan bahwa persepsi dari lingkungan sosial seseorang dapat secara signifikan membentuk niat pengguna untuk terlibat dengan platform pembelajaran online. Namun, tiga hipotesis lainnya menunjukkan pengaruh yang positif namun tidak signifikan, mengindikasikan bahwa variabel perilaku dan kontrol sikap tidak secara signifikan mempengaruhi penggunaan platform pembelajaran online. Secara kolektif, keempat variabel tersebut hanya menjelaskan 43,7% dari hasil perilaku, dengan 56,3% sisanya kemungkinan dipengaruhi oleh faktor-faktor lain di luar cakupan penelitian ini.

Kombinasi Metode SVM Dengan Optimasi SMOTE Terhadap Ulasan Pengguna Layanan Streaming Ni Putu Anik Juniantini; Yanti, Christina Purnama; Nilawati, Ni Ketut Utami
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2068

The development of digital technology has changed media consumption patterns in Indonesia, with streaming services like Disney+ Hotstar becoming increasingly popular. Since its launch in 2020, Disney+ Hotstar has offered exclusive content from Marvel, Pixar, Disney, National Geographic and others, quickly capturing the market in Indonesia. However, this service is not without controversy, particularly regarding certain content deemed to conflict with social values in Indonesia. Additionally, service quality, subscription prices, and content availability are also concerns for users. The difference in ratings on the Play Store (1.7) and the App Store (4.8) indicates a disparity in user satisfaction between the two platforms. This study aims to analyze user sentiment towards Disney+ Hotstar, particularly regarding reviews on the Play Store and App Store. Using a classification model with Support Vector Machine (SVM), optimized with the Synthetic Minority Over-Sampling Technique (SMOTE) to address data imbalance. Based on the analysis of 1,650 datasets, user sentiment tends to be neutral, as measured using the Vader Lexicon. The method testing results show that SMOTE optimization can improve the performance of the SVM model, with an accuracy increase of +0.7 on Play Store reviews from 0.67 to 0.74, and an accuracy increase of +0.11 on App Store reviews from 0.72 to 0.83 In conclusion, the SVM method optimized with SMOTE has proven effective in improving the accuracy of sentiment classification in user reviews of Disney+ Hotstar.

Penerapan Metode Stable Diffusion Dengan Fine Tuning Untuk Pola Endek Bali Ginantra, Ni Luh Wiwik Sri Rahayu; Hendrawati, Theresia; Wulandari, Dewa Ayu Putri
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2069

Endek Bali fabric is a cultural heritage of Bali renowned for its traditional decorative motifs, including floral, fauna, patra, and diamond patterns. Although rich in cultural value, artisans often face challenges in creating new designs that align with market trends while preserving cultural authenticity. Artificial Intelligence (AI) technology, particularly text-to-image generation models, offers a solution to this issue by streamlining the design process and enabling the exploration of new motifs. The Stable Diffusion model, introduced by Stability.AI in 2022 and open source, can be utilized to generate Endek Bali patterns through Fine Tuning techniques. Fine Tuning allows the model to be adapted to specific domains, enhancing its performance in generating textile patterns based on textual descriptions. This study aims to apply the Stable Diffusion model and Fine Tuning techniques to create new patterns and motifs. By using this model, it is hoped that innovative designs can be produced while maintaining the authenticity and local cultural values of Bali. The research demonstrates that the Fine-Tuned Stable Diffusion model is effective in creating Endek Bali patterns with high accuracy, as evaluated by Clip Similarity, with the highest scores achieved for Floral Patterns (92.43), followed by Decorative (free-form motifs) Floral (88.77), Decorative (free-form motifs) Geometric (87.94), and Decorative (free-form motifs) (85.79). These findings indicate the model’s flexibility and effectiveness in producing intricate textile designs, enabling designers and artisans to generate complex and innovative patterns solely from textual descriptions while preserving Bali’s cultural values.

Incorporation of Real Experiences and Artifacts As an Online Learning Intervention Perdana, Erda Guslinar; Hikmawati, Erna; Akhmadi
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2072

Online learning has become a significant trend in recent years, but the lack of direct interaction between students and lecturers can reduce its effectiveness. This study addresses this challenge by incorporating real-world experiences—specifically two case studies, one real-world project, and simulations—alongside concrete artifacts such as software documentation, research reports, and test results. This research aims to improve the effectiveness of online learning in software verification and validation courses by incorporating real-world experiences and artifacts. A case study approach was used to test the effectiveness of this intervention by involving students from related courses. Data were collected through interviews, observations, and surveys, and analyzed qualitatively and quantitatively to understand the impact of the intervention on improving the learning experience and understanding of software verification and validation concepts. Qualitative analysis highlighted that the integration of these elements improved students' conceptual understanding and practical application skills. Quantitative results indicated a 30% increase in student engagement and a 25% improvement in understanding course material compared to prior cohorts. The findings underscore the importance of integrating real-world experiences and artifacts into online learning to promote deeper engagement, better comprehension, and more authentic learning experiences.

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Home Page

OAI Link

Editorial Team

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Google Scholar

Contact Name Hadiansyah

Contact Email kanghadiansyah@plb.ac.id

Phone +6285220199772

Journal Mail Official tematik@plb.ac.id

Editorial Address Program Studi Manajemen Informatika Politeknik LP3I Bandung Jl. Pahlawan No. 59 Bandung 40123 Telp. (022) 2506500, Fax. (022) 2512564 Email : tematik@plb.ac.id

Location Kota bandung, Jawa barat INDONESIA

Abstract

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Contact Name
Hadiansyah

Contact Email
kanghadiansyah@plb.ac.id

Phone
+6285220199772

Journal Mail Official
tematik@plb.ac.id

Editorial Address
Program Studi Manajemen Informatika Politeknik LP3I Bandung Jl. Pahlawan No. 59 Bandung 40123 Telp. (022) 2506500, Fax. (022) 2512564 Email : tematik@plb.ac.id

Location
Kota bandung,

Jawa barat

INDONESIA