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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Information System Research
Poningsih Poningsih, Poningsih
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Sistem Pendukung Keputusan Penentuan Ranking Kenaikan Gaji Staff Dan Karyawan Poningsih, Poningsih
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (487.55 KB) | DOI: 10.30645/j-sakti.v3i2.136

Abstract

Currently AMIK and STIKOM Tunas Bangsa have approximately 100 employees (staff and employees). Each employee has a different and varied salary. Every year, AMIK and STIKOM Tunas Bangsa management provide salary increases to their employees. But the number of increases is very diverse. This decision support system will later provide recommendations to management in the form of employee performance ranking. There are several factors used in this decision support system, including work period, education and performance. The method used is Multi-objective Optimization on The Base of Ratio Analysis (MOORA). Where the advantages of MOORA are having a good level of selectivity because it can determine the objectives of the conflicting criteria.
Fuzzy Query Database Untuk Sistem Pendukung Keputusan Yang Cerdas Poningsih, Poningsih; Jalalludin, Jalalludin
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (622.144 KB) | DOI: 10.30645/j-sakti.v1i1.33

Abstract

The process of education and teaching is one of the acts of Tri Dharma College. The success of the learning process can not be separated from the role of a lecturer. Quality of faculty plays an important role in a college that wants to achieve the goal of teaching and learning processes that produce graduates (output) quality. Lecturer rated quality if it has the value of a good performance, which is reviewed from several aspects. This paper proposes an analysis based on the evaluation of faculty performance feedback using fuzzy query the database for intelligent decision support system. The value of diverse faculty performance, so as to the criteria of each lecturer is still ambiguous and still need to be clarified. Here will be determined three criteria of assessment of faculty performance is lacking, just and good. Of the three criteria are later obtained a recommendation to make a decision. Model rules obtained is the value of students and professors taken the maximum value, then rules obtained from students and professors taken minimum value, in order to obtain the value of the performance of lecturers as well as the criteria. Then the value of this performance, it can be used by institutions as advice on making a decision relating to a lecturer.
Sistem Informasi Jadwal Perkuliahan Menggunakan Media Televisi (Studi Kasus Pada Jurusan Teknik Informatika Upn “Veteran” Yogyakarta) Andani, Sundari Retno; Wibowo, Subastian; Poningsih, Poningsih
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2040.079 KB) | DOI: 10.30645/j-sakti.v1i1.34

Abstract

Students are always confused with a schedule of lectures, about time of lectures, room that will be used, even about come or not the lecturer on class. As a result, students must always go to the education department to inquire this issue. It is very ineffective. But there is no system that helps students in overcoming this problem. In this paper, the authors build a system with the title is the information system of the schedules of lectures using television. The system is built using Delphi 6.0 programming language and uses Microsoft Access 2003 as the database. To operate this system requires a CPU and a television screen as an output device. This system provides information on the schedule of lectures to the students through a television screen. Information provided includes schedule of lectures, room that will be used, the certainty of the lecture will take place, and informing announcements and activities that will take place. These systems also support the effectiveness of the performance of educational staff in the conduct of daily operations.
Sistem Pendukung Keputusan Penentuan Ranking Kenaikan Gaji Staff Dan Karyawan Poningsih, Poningsih
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v3i2.136

Abstract

Currently AMIK and STIKOM Tunas Bangsa have approximately 100 employees (staff and employees). Each employee has a different and varied salary. Every year, AMIK and STIKOM Tunas Bangsa management provide salary increases to their employees. But the number of increases is very diverse. This decision support system will later provide recommendations to management in the form of employee performance ranking. There are several factors used in this decision support system, including work period, education and performance. The method used is Multi-objective Optimization on The Base of Ratio Analysis (MOORA). Where the advantages of MOORA are having a good level of selectivity because it can determine the objectives of the conflicting criteria.
Sistem Pendukung Keputusan Memilih Facial Foam untuk Kulit Berminyak Pria dengan UTA Nainggolan, Darwis William; Silaban, Rido Syahputra; Damanik, Cantika Audy; Pakpahan, Clara Marsella; Poningsih, Poningsih
Bulletin of Information System Research Vol 3 No 1 (2024): December 2024
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v3i1.189

Abstract

Facial skin care in men, especially for those with oily skin, is gaining more and more attention in today's society. The problem of oily skin often leads to various problems, including acne and inflammation, which in turn can affect one's appearance and self-confidence. One solution to this problem is the use of Facial Foam, but choosing the right product is often a challenge, especially among men who are less familiar with skincare products. This research aims to develop a Decision Support System (DSS) using the Utility Theory Additive (UTA) Method to recommend Facial Foam that suits the characteristics of oily skin in men. The methodology used includes data collection regarding criteria, sub-criteria, and alternatives through observation and interviews with users. The five main criteria analysed were Product Effectiveness, Side Effects, Price, Packaging, and Availability. The results of the data analysis show that the Pond's Facial Foam product gets the highest rating of 3,443 so that this product makes it a recommended choice for men with oily skin. This research shows that the UTA Method proves to be effective in simplifying the multicriteria decision-making process related to skincare product selection, providing clear guidance for consumers to choose the product that best suits their needs.
Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques Siregar, Sandy Putra; Akbari, Imam; Poningsih, Poningsih; Wanto, Anjar; Solikhun, Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6410

Abstract

Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. Thus, max pooling was employed to reduce dimensionality while retaining crucial information. This paper offers an improved CNN through hyperparameter tuning and compares it to Transfer Learning models such as InceptionV3, NASNetMobile, and VGG16, which were chosen for their efficiency and accuracy. The dataset comprises 7,178 photos classified as Healthy, Leaf Late Blight, Septoria Leaf Spot, and Yellow Leaf Curl Virus, collected from Kaggle.. The dataset is separated into three sections: training, validation, and testing, with a ratio of 70:15:15. The results of this study revealed that the proposed method achieved the highest accuracy of 98.24%. In the application of transfer learning, the inceptionV3 model achieved an accuracy of 96.94%, whereas NASNetMobile obtained 97.50%, and VGG16 showed an accuracy of 96.76%. The evaluation is based on accuracy, precision, recall, F1-score and Inference time to determine the optimum model for accuracy and computing efficiency. This project uses the proposed method and Transfer Learning Techniques to categorize illness images on tomato leaves. These findings will drive further research to improve tehe performance of the proposed method for foliar disease classification and comparable applications.
A Hybrid GRG-Neighborhood Search Model for Dynamic Multi-Depot Vehicle Routing in Disaster Logistics Hartama, Dedy; Poningsih, Poningsih; Tanti, Lili
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.973

Abstract

In disaster relief logistics, timely and adaptive routing is critical to meet fluctuating demands and disrupted infrastructure. This paper proposes a Hybrid GRG–Neighbourhood Search (NS) model for solving the Multi-Depot Vehicle Routing Problem with Capacity and Time Dependency (MDVRP-CTD). The model integrates the Generalized Reduced Gradient (GRG) method for handling nonlinear capacity constraints and NS for local route refinement. The objective is to minimize total travel distance, delay penalties, and maximize vehicle utilization under dynamic disaster scenarios. Tested using the SVRPBench dataset, the hybrid model achieved up to 96.5% demand fulfillment, an 11% improvement in vehicle utilization, and a reduction in total distance by 7%, outperforming Tabu Search and ALNS in three simulation scenarios. The model demonstrates enhanced adaptability and responsiveness to time-sensitive, capacity-constrained environments. Its novelty lies in the integration of nonlinear optimization with adaptive local improvement tailored for disaster contexts, providing a robust decision-support tool for real-time humanitarian logistics.
Implementation of Random Forest Optimized with Ant Colony Optimization (ACO) for Breast Cancer Prediction Ht. Barat, Ade Ismiaty Ramadhona; Siregar, Sandy Putra; Poningsih, Poningsih; Windarto, Agus Perdana; Solikhun, Solikhun; Sembiring, Rahmat Widia
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.7116

Abstract

Breast cancer is a significant disease impacting women globally, highlighting the necessity for precise and dependable diagnostic models. This study aims to improve breast cancer prediction by optimizing the Random Forest algorithm using Ant Colony Optimization (ACO). This study uses datasets containing various cell characteristics to build and evaluate models. The ACO algorithm is applied to fine-tune the hyperparameters of the Random Forest model and improve its predictive performance. The experimental results showed that the optimized Random Forest model outperformed the baseline model in all evaluation metrics. The optimized model achieved an accuracy of 94.74%, precision of 97.92%, recall 90.38%, an F1 score of 92.93%, and an AUC score of 0, 9449 compared to the basic Random Forest model, with lower scores across all metrics. This improvement highlights the effectiveness of ACOs in improving model performance, especially in reducing false negatives, which are critical for medical diagnosis. This study demonstrates that ACO successfully fine-tunes Random Forest hyperparameters, achieving superior accuracy compared to baseline and outperforming previous optimization methods such as PSO. These findings confirm that the combination of Random Forest and ACO offers a powerful and effective approach to improving the accuracy of breast cancer predictions, making them a valuable tool for clinical decision-making.
Design of the expert system to analyze disease in Plant Teak using Forward Chaining Poningsih, Poningsih
International Journal of Artificial Intelligence Research Vol 1, No 1 (2017): June 2017
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (250.647 KB) | DOI: 10.29099/ijair.v1i1.11

Abstract

Teak is one kind of plant that is already widely known and developed by the wider community in the form of plantations and community forests. This is because until now Teak wood is a commodity of luxury, high quality, the price is expensive, and high economic value. Expert systems are a part of the method sciences artificial intelligence to make an application program disease diagnosis teak computerized seek to replace and mimic the reasoning process of an expert or experts in solving the problem specification that can be said to be a duplicate from an expert because science knowledge is stored inside a database  Expert System for the diagnosis of disease teak using forward chaining method aims to explore the characteristics shown in the form of questions in order to diagnose the disease teak with web-based software. Device keel expert system can recognize the disease after consulting identity by answering some of the questions presented by the application of expert systems and can infer some kind of disease in plants teak. Data disease known customize rules (rules) are made to match the characteristics of teak disease and provide treatment solutions.
Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset Poningsih, Poningsih; Windarto, Agus Perdana; Alkhairi, Putrama
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29509

Abstract

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.
Co-Authors Abdullah Ahmad Agus Perdana Windarto Akbari, Imam Alkhairi, Putrama Amalya, Nanda Ambariyanto Ambariyanto Bintang, Daud Damanik, Cantika Audy Eka Irawan Hartama, Dedy Hartama, Dedy Ht. Barat, Ade Ismiaty Ramadhona Irawan Irawan Jalalludin, Jalalludin Lili Tanti, Lili Lumbantobing, Dedy Kristianto Muhammad Safii Nainggolan, Darwis William Nainggolan, Joshua Fines Nasution, Siti Andry Yani Nur, Khairun Nisa Arifin Nur, Khairun Nisa Arifin Okprana, Harly Pakpahan, Clara Marsella Putri, Nirwanda Desnila Safii, M Sembiring, Rahmat Widia Silaban, Rido Syahputra Siregar, Abdul Zikri Siregar, Sandy Putra Solikhun Solikhun, Solikhun Sumarno . Sundari Retno Andani Sundari, Riski Wanto, Anjar Wibowo, Subastian Widodo Saputra