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Application of ant colony algorithm to optimize waste transport distribution routes in Tegal Gunawan, Gunawan; Handayani, Sri; Anandianskha, Sawaviyya
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5223

Effective and efficient waste management is an essential challenge in developing cities like Tegal City. Optimizing waste transport routes can reduce operational costs and environmental impact. This study aims to implement the Ant Colony Algorithm (ACO) to optimize waste distribution routes in Tegal City. This method was chosen for its proven ability to solve route optimization problems. This study developed a model for the simulation and analysis of waste transportation routes using actual location data from the Integrated Waste Treatment Site (TPST) to the landfill (TPA). The results showed that the implementation of ACO reduced the total mileage from 27.50 km to 21.05 km, a significant reduction that shows the algorithm's efficiency in determining the optimal route. The conclusion of this study confirms that ACO can be effectively used to improve waste transportation operations

Application of machine learning for election data classification in Tegal city based on political party support Andriani, Wresti; Gunawan, Gunawan; Naja, Naella Nabila Putri Wahyuning; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Elections are a critical aspect of democracy, where voter sentiment and political party support significantly influence outcomes. This study aims to predict election results in Tegal City using machine learning models, specifically Neural Networks, Random Forest, and Naive Bayes. Each algorithm was applied to a dataset containing demographic, polling, and Sentiment data to analyze political party support. The research revealed that Neural Networks outperformed other models in terms of accuracy (92%) and F1 scores for both positive (91%) and negative sentiments (92%). Random Forest and Naive Bayes, while effective, displayed lower overall performance. The findings highlight the value of utilizing advanced algorithms for local election sentiment analysis to help candidates adjust campaign strategies. This approach enhances understanding of voter behavior and supports more informed decision-making processes for the public and policymakers

Strategi Komunikasi Mengelola Persepsi Publik dalam Rebranding ACE Hardware ke AZKO Anandianskha, Sawaviyya
LUGAS Jurnal Komunikasi Vol. 9 No. 1: Juni 2025
Publisher : Institut Ilmu Sosial dan Manajemen STIAMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31334/lugas.v9i1.4910

Rebranding is a crucial strategy for companies to maintain relevance andcompetitiveness in an ever-evolving market. PT Aspirasi Hidup Indonesiaunderwent a significant rebranding effort by changing its name and brandidentity from ACE Hardware to AZKO. The primary objective was toidentify the key messages crafted within the rebranding communicationstrategy, including the elements used to build AZKO's new brand image. Aqualitative approach with a descriptive method was applied through directobservation, document analysis, and literature review. Findings revealthat AZKO's rebranding communication focused on three core elements:innovation, inspiration, and local relevance. The message "Your HomeLife Improvement Partner" was consistently conveyed through visualelements such as a new logo featuring an open circle, minimalist stagedesigns at the Bundaran HI launch event, and digital media, including thelargest videotron in Jakarta at Mall Taman Anggrek. The launch event,attended by thousands, included a symbolic moment with spotlightprojections of "ACE berubah jadi AZKO" (ACE changes to AZKO). Thecommunication strategy also leveraged influencer collaborations toexpand its reach. In conclusion, AZKO's rebranding communicationstrategy successfully established a new, relevant identity positivelyreceived by the audience. The consistency of messaging, the strength ofvisual elements, and a focus on local relevance were key factors in itssuccess. These findings contribute to the literature on rebrandingcommunication strategies and offer practical guidance for othercompanies undergoing similar brand transitions

STRATEGI KOMUNIKASI PEMASARAN TEH TONG TJI MELALUI INSTAGRAM DALAM MEMBANGUN BRAND RECOGNITION Anandianskha, Sawaviyya; Harahap , Halomoan
Konvergensi Vol 6 No 1 (2025): Konvergensi: Jurnal Ilmiah Ilmu Komunikasi (Juni 2025)
Publisher : Program Studi Ilmu Komunikasi Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/gfx67n74

Digital transformation has reshaped marketing practices, with Instagram emerging as a key platform for building brand recognition. This study examines the marketing communication strategy of Teh Tong Tji on Instagram, emphasizing the use of Reels, Stories, and visual elements to enhance audience engagement. A qualitative approach, guided by Social Media Engagement Theory (SME), was employed through in-depth interviews, content observation, and document analysis. Findings reveal that audience engagement remains limited due to inconsistencies in delivering relevant and appealing content. Among the available features, Reels and Stories proved most effective in generating higher interaction rates, including likes, comments, and shares. Visual storytelling, particularly product introduction videos, was found to play a significant role in strengthening brand recognition. Thematic analysis further highlights the necessity of aligning content strategies with audience preferences, as well as the importance of ongoing evaluation to optimize algorithm performance. This study contributes to the digital marketing literature by offering insights into how traditional brands can adapt their communication strategies to remain competitive in the social media era. Practical recommendations include enhancing content consistency, leveraging interactive features, and refining visual elements to better meet audience needs and foster stronger brand recognition.

Decision Support System to assess customer satisfaction using Analytical Hierarchy Process Andriani, Wresti; Gunawan, Gunawan; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i4.163

Transportation is an important aspect of mobility or global movement and activities. As public transportation that can be accessed online by the public, Gojek and Grab types of transportation provide transportation services and are growing rapidly. At the time of Covid 19 around 2020, online transportation was very important and much sought after. More and more online transportation companies are appearing, especially in Tegal City, so that there are more service offerings that consumers can use. User or consumer satisfaction measurements were carried out using Fuzzy Logic Method Analytical Hierarchy Process (AHP) on 200 consumers who used Gojek or Grab or other online transportation for 3 to 4 months in 2022 in Tegal City. The results obtained by customers or consumers were satisfied with Gojek transportation at 45%, with male consumers at 67%, and Grab at 37%, with male consumers at 65%, followed by other online transportation (X and Y). These results can be used as an option for consumers who expect the best service.

Application of sma method and ahp to predict the level of tidal flood vulnerability in Tegal City Nugroho, Bangkit Indarmawan; Farkhan, Muhammad; Anandianskha, Sawaviyya; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.235

This study examines the application of the Simple Moving Average (SMA) and Analytic Hierarchy Process (AHP) methods to predict tidal flood vulnerability in Tegal City. The objective is to develop a more accurate prediction method for tidal flood vulnerability. The methods used are a combination of SMA and AHP. The results indicate that this combination is effective in producing more accurate predictions compared to conventional methods. Villages such as Muarareja, Tegalsari, Mintaragen, and Panggung have been identified as highly vulnerable and require more intensive mitigation. The implications highlight the importance of a multi-method approach to understanding complex phenomena like flood vulnerability. For future research, it is recommended to integrate real-time weather data and consider socio-economic factors to enhance accuracy and relevance in disaster mitigation. The findings are expected to assist in better urban planning and resource allocation, as well as improve community resilience against tidal flood disasters.

Application of the nearest neigbour interpolation method and naives bayes classifier for the identification of bespectacled faces Murtopo, Aang Alim; Januarto, Sigit; Anandianskha, Sawaviyya; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.242

Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology

Application of expert system using certainty factor method to identify diseases in rice plants Azmi, Isni; Gunawan, Gunawan; Anandianskha, Sawaviyya
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.280

This article explores the application of expert systems using certainty factor methods for disease identification in rice crops, highlighting the importance of information technology integration in agriculture. The study aims to develop a system that allows quick and accurate identification of rice disease, using certainty factor methods that are effective in dealing with data uncertainty. This study used a quantitative approach with a quasi-experimental design. The results indicate an effective system for identifying diseases, with significant implications for supporting farmers and improving food security. Suggestions for future research include system integration with mobile applications and real-time data analysis to improve system accessibility and applicability in modern agricultural practices.

Prediction of Bank Central Asia stock prices after dividend distribution using ARIMA method Surorejo, Sarif; Sulthon, Muhammad; Anandianskha, Sawaviyya; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.294

This study explores the prediction of Bank Central Asia (BBCA) stock prices following the annual dividend distribution using the Autoregressive Integrated Moving Average (ARIMA) method. The primary goal is to provide a robust forecasting tool to aid investors and financial analysts in making informed decisions. The research employs a quantitative approach with a quasi-experimental design, analyzing weekly BBCA stock price data from January 2019 to February 2024. The findings demonstrate that the ARIMA (2, 1, 2) model provides stable and reliable predictions of BBCA stock prices, showing slight weekly variations but overall stability. Practically, these predictive models can be integrated into a web-based platform, allowing real-time stock price forecasting and broader accessibility for users. This study contributes to the financial literature by validating the ARIMA model's applicability in the Indonesian stock market and suggesting the exploration of hybrid models and external economic factors for future research.

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies Gunawan, Gunawan; Andriani, Wresti; Anandianskha, Sawaviyya; Murtopo, Aang Alim; Nugroho, Bangkit Indarmawan; Naja, Naella Nabila Putri Wahyuning
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47935

Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.

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