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Nurdin Nurdin
Universitas Malikussaleh
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Application of Triple Exponential Smoothing Method for Predicting the Number of Patients at RSUD dr.Fauziah Bireuen Maysura; Nurdin; Nunsina
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID487

Abstract

RSUD dr. Fauziah Bireuen is the main referral hospital in Bireuen Regency which has an important role in public health services. Fluctuations in the number of patients per polyclinic are a challenge in managing resources such as medical personnel, medicines, and other supporting facilities. This study aims to apply the triple exponential smoothing method in predicting the number of patients per polyclinic. The results showed that the triple exponential smoothing method has a high level of accuracy with a MAPE value of 1.456% (98.544% accuracy). Predictions using triple exponential smoothing predict 211,460 patients in January 2025, 211,454 in February 2025, and 211,455 for March 2025 to December 2026. Based on these results, triple exponential smoothing is recommended as it provides accurate results and supports the hospital's operational efficiency.
Clustering Analysis to Identify Stunting Vulnerability Areas in North Aceh District Using the Fuzzy C-Means Algorithm Muhammad Ridha; Nurdin; Maryana
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.14892

Abstract

Stunting is a serious public health issue that poses a long-term threat to the quality of human resources. North Aceh Regency is one of the regions with a relatively high prevalence of stunting, requiring targeted and effective intervention strategies. This study aims to classify regions based on their level of stunting vulnerability to support data-driven decision-making. The Fuzzy C-Means (FCM) clustering algorithm was selected due to its ability to handle data with flexible membership degrees, making it suitable for complex classification tasks. The data used in this research were obtained from the North Aceh Health Office for the year 2023 and include variables such as the number of children recorded in the E-PPGBM system, newly entered children in 2023, and the percentages of stunting, wasting, and underweight across 32 subdistricts. The research process involved data collection, literature review, system design and implementation using the Python programming language, and analysis of clustering results. The findings reveal that the 32 subdistricts can be grouped into three main clusters: high vulnerability (13 subdistricts), medium vulnerability (6 subdistricts), and low vulnerability (13 subdistricts). These clusters facilitate the visualization and identification of priority areas requiring more focused stunting interventions. In conclusion, the FCM algorithm proved effective in clustering regions based on stunting-related data. The implication of this study is to provide a foundation for local governments in formulating more efficient and targeted stunting intervention strategies according to the vulnerability level of each area.
Determining Eligibility for Smart Indonesia Program (PIP) Recipients Using the Backpropagation Method Rizkya, Ghinni; Nurdin, Nurdin; Meiyanti, Rini
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9733

Abstract

The government provides financial assistance, educational opportunities, and expands access for students from poor or vulnerable families through the Smart Indonesia Program (PIP). At Madrasah Ibtidaiyah Negeri 20 Bireuen, the selection process for underprivileged students is still carried out manually by homeroom teachers by collecting data on students and their parents. This study aims to design, implement, and evaluate a classification method using the Backpropagation Neural Network to determine the eligibility of PIP scholarship recipients. The dataset consists of 309 entries, comprising 217 training data and 92 testing data, collected from MIN 20 Bireuen students between 2021 and 2023. The attributes used include father's occupation, mother's occupation, father's income, mother's income, number of dependents, number of vehicles, home ownership status, and card ownership status. Prior to training, the data were normalized using Min-Max scaling. The model was built with one hidden layer using a hard-limit activation function and a learning rate of 0.01. The classification results are categorized as "Eligible" and "Not Eligible". The model achieved an accuracy of 98%, precision of 100%, recall of 95%, and F1-score of 97%.
Real-Time Detection of Coffee Cherry Ripeness Using YOLOv11 Ilyana, Anis; Nurdin, Nurdin; Maryana, Maryana
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9735

Abstract

This study aims to develop a real-time coffee fruit ripeness detection system using the YOLOv11 algorithm to assist farmers in determining the optimal harvest time. The dataset comprises 302 images categorized into three ripeness levels: ripe, semi-ripe, and unripe. Model training was conducted on Google Colab with data augmentation to enhance dataset variability and prevent overfitting. After 20 epochs, the model demonstrated strong performance in the ripe category (mAP50: 0.774, Precision: 0.645, Recall: 0.812) and satisfactory results for semi-ripe fruits (mAP50: 0.695, Precision: 0.624, Recall: 0.679). However, detection performance for unripe fruits was lower (mAP50: 0.4). The system achieved an inference time of 183.4 ms per image, with fast preprocessing and postprocessing (0.5 ms each), indicating its suitability for real-time applications. While the model performs well overall, further improvement is needed in detecting unripe coffee fruits for enhanced system effectiveness.
Implementation of Ant Colony Optimization (ACO) Algorithm for Route Optimization of Tourist Paths in Takengon Suryana, Fitra; Nurdin, Nurdin; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9706

Abstract

This study aims to design and implement a system for determining the shortest route between tourist destinations in Takengon using the Ant Colony Optimization (ACO) algorithm. The system is developed to assist travelers in obtaining efficient visitation routes based on distance and travel time. Experiments were conducted on 20 tourist locations, resulting in an optimized route with a total travel distance of 40.40 km and an estimated travel time of 81 minutes. The computation process took only 0.024001 seconds with a memory usage of 20.23 KB. The ACO algorithm was executed using 10 ants with key parameters set to alpha (α) = 1, beta (β) = 2, and rho (ρ) = 0.5. ACO demonstrated high effectiveness in exploring route combinations and iteratively generating near-optimal solutions. The chosen parameters were determined through experimentation to balance solution quality and convergence speed. In addition to generating the optimal visitation sequence, the system also provides complete turn-by-turn navigation instructions, including major roads such as Jalan Lintas Tengah Sumatera and Jalan Lebe Kader. The actual estimated travel route based on the generated navigation covers a distance of 97.4 km with a travel duration of approximately 2 hours and 42 minutes. The results indicate that ACO is an effective and efficient approach for solving medium- to large-scale tourist route optimization problems. The developed system can serve as a practical tool in the tourism sector and has the potential to be adapted and implemented in other tourist regions with similar routing challenges.
Comparison of K-Means and K-Medoids Methods in Clustering High Population Density Areas in Bireuen Regency Andri Alfitra; Nurdin, Nurdin; Meiyanti, Rini
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15602

Abstract

This study examines the population density distribution in Bireuen Regency by applying two clustering algorithms, namely K-Means and K-Medoids, to demographic data from 2019 to 2023. Three main variables were used: total population, number of ID card holders (KTP), and number of households (KK). The clustering results identified three primary groups: very dense, dense, and not dense. Districts such as Kota Juang, Jeumpa, and Peusangan consistently fell into the very dense category, while districts like Pandrah, Gandapura, and Makmur tended to be classified as not dense. Cluster quality was evaluated using the Davies-Bouldin Index (DBI). The evaluation results showed that the K-Means algorithm performed better in most years analyzed, particularly in 2020 with the lowest DBI value of 0.3906. Meanwhile, in 2023, K-Medoids outperformed K-Means, with a DBI value of 0.7724. These findings indicate that K-Means is more effective in handling homogeneous data, whereas K-Medoids is more adaptive to data containing outliers or irregular patterns. Overall, the choice of clustering method depends on the characteristics of the data used. The results provide a spatial overview of population distribution that can support regional planning and data-driven public policy. These findings are expected to serve as a basis for more targeted and equitable regional development planning. For future research, it is recommended to expand the analysis by including additional variables such as area size and socioeconomic indicators, as well as optimizing the number of clusters using methods like the Elbow method or Silhouette Score.
Development of an IoT-Based Smart Greenhouse with Fuzzy Logic for Chrysanthemum Cultivation Khairina, Jikti; Nurdin, Nurdin; Fikry , Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10313

Abstract

Conventional cultivation of Chrysanthemum plants in greenhouses faces serious challenges such as inefficiency, response delays, and errors in temperature and humidity settings due to manual management. These conditions result in unsuitable growing environments that can reduce the quality and quantity of harvests. To overcome these problems, this study developed a smart greenhouse system based on the Internet of Things (IoT) and cloud computing with the application of fuzzy logic. The system is designed to automatically monitor and control temperature, humidity, and light intensity using NodeMCU ESP32, DHT22 and BH1750 sensors, as well as relay-based actuators and mini air conditioners. Environmental data is sent to the cloud and processed using the Sugeno fuzzy method to produce adaptive and precise control decisions. Test results show that the system can maintain stable and optimal environmental conditions with an average temperature control difference of 30.341% and an actuator efficiency of 9.34% against microcontroller commands. This system provides a modern solution to the limitations of traditional methods, and supports smart agriculture in tropical climates such as Lhokseumawe.
Implementation of Clustering Method Using K-Means Algorithm for Grouping BPJS Health Patient Medical Record Data Sapitri, Anggri; Nurdin, Nurdin; Afrilia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10046

Abstract

Clustering medical record data of BPJS Health patients is essential in supporting data-driven decision-making in hospitals. This study aims to implement the K-Means algorithm to cluster patient medical records at RSUD Simeulue based on BPJS class and patient address variables. The data were first normalized using the Z-Score method to standardize variable scales, followed by the iterative application of the K-Means algorithm until convergence was reached at the sixth iteration. The study employed three Cluster, namely Cluster 1 (Very Many), Cluster 2 (Many), and Cluster 3 (Not Many). The final results show that Cluster 1 contains 258 patients from Class 1 and 292 from Class 2; Cluster 2 consists of 296 patients from Class 2; and Cluster 3 includes 101 patients from Class 1, 115 from Class 2, and 148 from Class 3. In addition to classification by BPJS class, clustering based on patient address revealed a dominant distribution from Simeulue Timur, Teluk Dalam, and Teupah Selatan sub-districts. The clustering results were implemented into a web-based information system using the Laravel framework and MySQL database, enabling hospital administrators to visualize and analyze patient data effectively. This study demonstrates that the K-Means algorithm can be effectively applied in classifying medical record data to support healthcare management decision-making.
Z-Score Based Initialization for K-Medoids Clustering: Application on QSAR Toxicity Data Nurdin, Nurdin; Amalia, Nova; Fajriana, Fajriana
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10448

Abstract

The efficiency of clustering algorithms significantly depends on the initialization quality, especially in unsupervised learning applied to complex datasets. This study introduces an enhanced K-Medoids clustering approach using Z-Score-based medoid initialization to improve convergence speed and cluster validity. The method was evaluated using the QSAR Fish Toxicity dataset, consisting of 908 instances and seven numerical features. Initial medoids were selected based on standardized Z-Score values, resulting in a substantial reduction in convergence time from an average of 6 iterations to just 2. Clustering performance was assessed using three internal validation metrics: Davies-Bouldin Index (DBI), Silhouette Coefficient (SC), and Calinski-Harabasz Index (CHI). The DBI score decreased from 1.7328 to 0.8768, indicating improved cluster compactness and separation. In parallel, the SC increased from 0.327 to 0.619, and the CHI rose from 214.75 to 562.43, confirming more coherent and well-separated clusters. These results demonstrate that Z-Score-based initialization significantly boosts the robustness of K-Medoids, offering a simple yet effective strategy for unsupervised partitioning, particularly in toxicological and biochemical data analysis.
Information Systems And Information Technology Strategies In The EMIS (Education Management Information System) Khaidar, Al; Azzanna, Maghriza; Rahmad, Rahmad; Hasibuan, Arnawan; Daud, Muhammad; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7639

Abstract

Perkembangan teknologi informasi telah memengaruhi pengelolaan data pendidikan melalui sistem informasi manajemen, salah satunya Education Management Information System (EMIS). Penelitian ini bertujuan untuk menganalisis efektivitas implementasi EMIS di MAN 1 Aceh Timur serta faktor-faktor yang memengaruhi keberhasilannya. Metode penelitian menggunakan pendekatan kualitatif interaktif dengan studi kasus, melibatkan kepala madrasah, operator EMIS, dan pihak terkait sebagai informan. Analisis dilakukan menggunakan metode SWOT dan value chain untuk mengevaluasi kekuatan, kelemahan, peluang, dan ancaman implementasi sistem. Hasil penelitian menunjukkan EMIS memiliki potensi meningkatkan efektivitas pengelolaan data, integrasi informasi, dan mendukung pengambilan keputusan. Namun, sistem mengalami kendala teknis, terutama gangguan server dengan frekuensi bervariasi setiap bulan, puncaknya terjadi pada Maret dan Juli masing-masing 5 kali, dengan durasi rata-rata meningkat dari 1,8 jam di Januari menjadi 2,5 jam di Juli dan terendah 1,0 jam di April. Evaluasi menekankan perlunya peningkatan infrastruktur, pelatihan operator, dan koordinasi antar pihak terkait untuk mengoptimalkan kinerja EMIS di masa depan.
Co-Authors - Miranda ., Muthmainah Adi Prasetyo Afrilia, Yesy Aidilof, Hafizh Al Kautsar Al Khaidar Alaiya, Azna Alqhifari, Azka Ama Zanati Amalia, Nova Amin Munthoha Aminsyah, Ansharulhaq Ananda Faridhatul Ulva Andri Alfitra Anggara, Aji Arnawan Hasibuan Aynun, Aynun Aynun, Nur Azzanna, Maghriza bhakti wan khaledy Bustami Bustami Bustami Bustami Cesilia, Yolinda Chaeroen Niesa Chicha Rizka Gunawan Cut Agusniar Dadang Priyanto Dahlan Abdullah Darmansyah, Arif Desky, Muhammad Aulia Dewi Astika Erni Susanti Eva Darnila Fadlisyah Fadlisyah Fadlisyah Fahrozi, Fazar Fajriana Fajriana Fajriana, Fajriana Fasdarsyah Fasdarsyah fatimah Fatimah Fikhri, Aditya Aziz Fikran, Rifzan Fikri Fikri Fikry , Muhammad Gavinda, Virza Ginting, Andriyan gunawan, chicha rizka Gunawan, Chichi Rizka Hafizh Al Kautsar Aidilof Hafizh Al-Kautsar Aidilof Hamdhana, Defry Herman Fithra Hermansyah Hermansyah I Made Ari Nrartha Ilyana, Anis Imanda, Nanda Intan Nuriani Isa, Muzamir Ismun Naufal Jessika, Jessika Jikti Khairina Julia Ulfah Khaidar, Al Khairina, Jikti Khairul Khairul, Khairul Khairuni Khairuni Kurnia, Sri M Farhan Aulia Barus M Rizwan M Suhendri M. Ali, Rahmadi Marleni Marleni Maryana Maryana Maryana Maryana Maryana Maryana Maryana, Maryana Maulita, Maya Maya Juwita Dewi Maysura Meriatna Meriatna Muchlis Abdul Muthalib Muhammad Daud Muhammad Faisal Muhammad fauzan Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Hutomi Muhammad Iqbal Muhammad Johan Setiawan Muhammad Nasir Muhammad Riansyah Muhammad Ridha Mukti Qamal Muliana, Syarifah Munirul Ula Mutammimul Ula Muzakir Nur Nadilla Baimal Puteri NELI SUSANTI, NELI Nunsina, Nunsina Nur, Muzakir Pradita, Cindy Cika Rahmad Rahmad Rahmad Rahmat Rahmat Raihan Putri Rasyada, Reza Dian Reza, Restu Rini Meiyanti Risawandi, Risawandi Riza Mirza Rizal S.Si., M.IT, Rizal Rizki Setiawan Rizki Suwanda Rizky Putra Fhonna Rizkya, Ghinni Robi Kurniawan Rusadi, Athirah salamah salamah Salimuddin, Salimuddin Salsabila, Thifal Samudera, Brucel Duta Sapitri, Anggri Sari, Cut Jora Sayuti, Muhammad Siagian, Tania Annisa Siregar, Widyana Verawaty Sri Kurnia Suci Fitriani, Suci Suhaili Sahibul Muna Sujacka Retno Sultan, Kana Suryana, Fitra Syandriani Harahap Taufik Taufik Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Uci Mutiara Putri Nasution Ulva Fitriani Wahdana, Aldi Wan, Syahputra Wawan Wawan Yani, Muhamamd Yeni Yeni Yesy Afrilia Yesy Afrillia Yulisda, Desvina Zahrah, Violita Aditya Zahratul Fitri Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar Zuraida Zuraida