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SIKAP REMAJA SURABAYA TERHADAP GAMBARAN LAKI-LAKI DALAM IKLAN L-MEN VERSI GAIN MASS 2012 Chandra, Rudy
Jurnal e-Komunikasi Vol 1, No 3 (2013)
Publisher : Jurnal e-Komunikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (78.005 KB)

Penelitian ini dilakukan untuk mengetahui sikap remaja Surabaya terhadap gambaran laki-laki dalam iklan L-Men versi Gain Mass 2012. Sikap remaja Surabaya dilihat dari komponen kognitif, afektif dan konatif. Gambaran laki-laki dalam iklan L-Men versi Gain Mass 2012 meliputi tubuh atletis, riasan tertentu, aktifitas di luar dan disukai wanita. Jenis penelitian adalah deskriptif dengan pendekatan kuantitatif. Metode yang digunakan adalah metode survei dengan teknik kuesioner dan wawancara. Jumlah responden sebanyak 100 orang dan hasil penelitian ini menunjukkan bahwa sikap remaja Surabaya terhadap gambaran laki-laki dalam iklan L-Men versi Gain Mass 2012 adalah netral.

Analisis Pemilihan Saham oleh Investor Asing di Bursa Efek Indonesia CHANDRA, RUDY
BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi Vol. 17, No. 2
Publisher : UI Scholars Hub

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The first objective of this study is to analyze what kind of sector industry which is selected by foreign investor in Indonesia. Second, to analyze what kind of financial firm characteristic of stocks which is selected by foreign investor. This study used a quantitative approach by using secondary data from financial reports and historical data from Indonesian Stock Exchange. Researcher used multivariate regression to analyze the correlation between stocks selected by foreign investor with financial firm characteristic. Based on the result, foreign investor are overweight stocks from Consumer Goods Industry. Dividend yield, beta, and book to market are financial firm characteristic which is significantly influence the stock selection by foreign investor.

Forward Selection Attribute Reduction Technique for Optimizing Naïve Bayes Performance in Sperm Fertility Prediction Lubis, Ahmadi Irmansyah; Chandra, Rudy
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11967

The problem of infertility between husband and wife is an important issue that destroys family harmony, and many people still consider infertility or infertility a female problem. However, about 7% of men of childbearing age suffer from infertility. The biggest factor causing male infertility is sperm quality problems. Sperm analysis can be the best predictor of male fertility potential. Machine learning and data mining techniques can be used to automate disease diagnosis. This study aims to obtain a regular form classification model from sperm sample data of 100 volunteers. This classification model can be used to predict male fertility levels into 2 classes, namely normal and alter (decreased fertility). This study uses a fertility dataset obtained from the UCI Machine Learning Repository. Before the data mining process, data preprocessing is required. The classification process is carried out using Naive Bayes and attribute reduction techniques using forward selection to see the increase in the accuracy of Naive Bayes performance. The Naive Bayes test without attribute reduction has an accuracy of 85%, while attribute reduction with forward selection has an accuracy of 88% in predicting sperm fertility. Therefore, by using forward selection with Naive Bayes to reduce attributes in this study, this study was able to increase accuracy by 3% and can be used to help predict sperm fertility

Dinamika Regulasi Komunikasi Publik dalam Sistem Sosial: Perspektif Autopoiesis Luhmann Chandra, Rudy; Sazali, Hasan
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3420

This study aims to analyze the dynamics of public communication regulation in the social system from the perspective of Luhmann's autopoiesis. This study uses a qualitative method with a library research approach. The data of this study is sourced from relevant literature. Data analysis is carried out by reducing data, presenting data, and drawing conclusions. The validity of the data in this study was carried out through a credibility test. The results of this study show that in the dynamics of public communication regulation in the social system based on Luhmann's autopoiesis perspective, public communication regulation functions to maintain a balance between information disclosure, control, and system adaptability to rapid social change. Luhmann emphasized that public communication systems must continue to produce and reproduce elements that ensure their functions continue to function, such as transparency, trust, and responsiveness. Adaptive regulations allow the government to interact effectively with the public, especially in the digital era which presents challenges such as misinformation, disinformation, and rapid information flow. Cross-sector collaboration and regulatory flexibility are needed to maintain social stability and strengthen the relationship between the government and society, with information disclosure as a key pillar. However, the regulation must also include privacy protection and control over the spread of invalid information, so that public communication systems remain relevant, accountable, and able to adapt to evolving social dynamics.

Dinamika Regulasi Komunikasi Publik dalam Sistem Sosial: Perspektif Autopoiesis Luhmann Chandra, Rudy; Sazali, Hasan
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3420

This study aims to analyze the dynamics of public communication regulation in the social system from the perspective of Luhmann's autopoiesis. This study uses a qualitative method with a library research approach. The data of this study is sourced from relevant literature. Data analysis is carried out by reducing data, presenting data, and drawing conclusions. The validity of the data in this study was carried out through a credibility test. The results of this study show that in the dynamics of public communication regulation in the social system based on Luhmann's autopoiesis perspective, public communication regulation functions to maintain a balance between information disclosure, control, and system adaptability to rapid social change. Luhmann emphasized that public communication systems must continue to produce and reproduce elements that ensure their functions continue to function, such as transparency, trust, and responsiveness. Adaptive regulations allow the government to interact effectively with the public, especially in the digital era which presents challenges such as misinformation, disinformation, and rapid information flow. Cross-sector collaboration and regulatory flexibility are needed to maintain social stability and strengthen the relationship between the government and society, with information disclosure as a key pillar. However, the regulation must also include privacy protection and control over the spread of invalid information, so that public communication systems remain relevant, accountable, and able to adapt to evolving social dynamics.

Design and Implementation of the Shortest Path Navigation in Samosir District using Branch and Bound Algorithm Chandra, Rudy; Arifin Prasetyo , Tegar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5585

Samosir has a large area and several tourist attractions, making it difficult for visitors to explore the island. Unknowing the route can make the journey less fun and waste time. In general, tourists seek to know the fastest way to a tourist location to save time and money while on vacation. As a result, we require an application that will offer directions to the shortest path. This research aims to develop a web-based application that may display a map of the shortest travel to a tourist site. This website will display a map that marks the route from the origin point to the destination point. The Branch and Bound algorithm is used to determine the shortest path. The Python libraries OSMnx, Folium, and NetworkX modify paths and show a route map with OpenStreetMap. The error value of the distance between the branch, the bound algorithm, and Google Maps is used to obtain the RMSE accuracy value. The RMSE value is 3.02 and the MAPE value is 0.0023 indicating that the application produced already has a good implementation prototype. Furthermore, there is no significant distinction between the appearance of maps that implement OpenStreetMap and Google Maps.

Web-based decision support system for boarding house selection according to preferences in Kefamenanu (BTN) area using Tsukamoto Fuzzy Logic Method Chandra, Rudy; Kelen, yoseph Pius Kurniawan; Manek, Patricia Gedrudis; Septian Manek, Siprianus
Jurnal Simantec Vol 14, No 1 (2025): Jurnal Simantec Desember 2025 (Article in Progress)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v14i1.29719

The problem in searching for boarding houses in the Kefamenanu (BTN) area is the many choices with different variations in price, facilities, distance, and comfort levels, making it difficult for prospective residents to determine a boarding house that suits their personal preferences. This study aims to build a web-based decision-making system that can help users choose a boarding house according to their interests quickly and accurately. The method used is the Tsukamoto Fuzzy Logic method, which is able to accommodate uncertainty and subjectivity in the assessment process of boarding house selection criteria. The system is designed with several fuzzy variables such as price, distance, facilities, and comfort, which are processed through fuzzy rules in the form of IF-THEN to produce the best boarding house recommendations. The novelty of this study lies in the application of the Tsukamoto method in the context of selecting boarding houses in Kefamenanu, which has not previously been widely developed using a fuzzy logic approach. The contribution of this study is to produce a web-based platform that is able to accelerate the process of searching for boarding houses that suit user needs, while increasing accuracy and satisfaction in decision making. The results of the study showed that the system was able to recommend boarding house options with a level of user preference matching reaching 90%, based on the results of trials on 30 respondents in the BTN Kefamenanu area.Keywords: Web-Based Application, Fuzzy Logic, Tsukamoto Method, Boarding House Selection, Decision Support System

Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm Chandra, Rudy; Arifin Prasetyo , Tegar; Lumbangaol, Heni Ernita; Siahaan, Veny; Sianipar, Johan Immanuel
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1296

Caladium is a popular ornamental plant and has business potential. However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. The data set used is 1200 data with four classes; each class has 300 data. The best model is found with the parameter epoch 100, learning rate 0.001, and batch size 64. The model is then implemented in a mobile application with two menus, "Take a photo" and "Choose an image," which will display the classification output and confidence values of the four types of Caladiums. Testing with 30 test data per class achieves 0.975 accuracy on both menus. On the “Take a photo” menu, precision is 0.974, recall is 0.9725, and f1-score is 0.965. Meanwhile, on the “Choose an image” menu a precision and recall value is 0.975, and f1-score value of 0.97.

Optimizing parameter selection in bidirectional encoder portrayal for transformers algorithm using particle swarm optimization for artificial intelligence generate essay detection Prasetyo, Tegar Arifin; Chandra, Rudy; Siagian, Wesly Mailander; Siregar, Horas Marolop Amsal; Siahaan, Samuel Jefri Saputra
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5543-5554

This research proposes a novel method for detecting artificial intelligence (AI)-generated essays by integrating the bidirectional encoder representations from transformers (BERT) model with particle swarm optimization (PSO). Unlike traditional approaches that rely on manual hyperparameter tuning, this study introduces a systematic optimization technique using PSO to improve BERT’s performance in identifying AI-generated content. The key problem addressed is the lack of effective, real-time detection systems that preserve academic integrity amidst rapid AI advancements. This optimization enhances the model’s detection accuracy and operational efficiency. The research dataset consisted of 46,246 essays, which, after data cleaning, were refined to 44,868. The model was then tested on 9,250 essays. Initial evaluations showed BERT's accuracy ranging from 83% to 94%. After being optimized with PSO, the model achieved an accuracy of 98%, an F1-score of 98.31%, precision of 97.75%, and recall of 98.87%. The model was deployed using a FastAPI-based web interface, enabling real-time detection and providing users with an efficient way to quickly verify text authenticity. This research contributes a scalable, automated solution for AI-generated text detection and offers promising implications for its application in various academic and digital content verification contexts.

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