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Jurnal Komtekinfo
Published by Universitas Putra Indonesia YPTK Padang
ISSN : 23560010     EISSN : 25028758     DOI : DOI: 10.35134/komtekinfo.v9i2.1
Core Subject : Science,
Computer Science & IT
Software Engineering, Multimedia, Artificial intelligence, Data Mining, Knowledge Database System, Computer network, Information Systems, Robotic, Cloud Computing, Computer Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 244 Documents
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Perancangan Sistem Customer Relationship Management (CRM) Berbasis Web pada Senyaman Resto & Coffee Rahim, Muhamad Aulia; M, Mardison; Jamhur, Annisak; Rani, Larissa Navia
Jurnal KomtekInfo Vol. 11 No. 4 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i4.615

Abstract

Marketing or promotional activities that are still less effective are a business obstacle, especially at Senyaman Resto & Coffee. This can be seen from the large amount of sales transaction data, sales transaction data is still recorded in books and records. Based on this, this research aims to design a Customer Relationship Management (CRM) system to increase promotional activities and improve services to Senyaman Resto & Coffee consumers. This CRM system design was built using PHP and MySql programming. CRM system design also adopts design tools using Unified Modeling Language (UML). Based on performance testing of the CRM system that has been designed, it appears that the system can provide features for creating sales and customer reports. These results had quite an impact on sales data processing which was built better than before. The contribution of this research also provides efficiency in the promotion and sales management processes that occur at Senyaman Resto & Coffee
Technology Readiness Index untuk Menganalisis Kesiapan Adopsi Teknologi Kecerdasan Buatan Mahasiswa Komputer Wirahmadayanti, Isna; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.584

Abstract

The education sector combined with the branch of artificial intelligence has great potential to change the way information is accessed and managed to improve the learning experience and support decision making in the educational process. It is important to understand the level of readiness for the adoption of artificial intelligence among students as the main stakeholders in the educational environment. The purpose of this study was to determine the readiness for adoption of technology, and what factors influence the readiness for adoption of artificial intelligence in Computer Science Students at Universitas Putra Indonesia "YPTK" Padang. This study uses the Technology Readiness Index (TRI) method which consists of four variables, including the variables of optimism, innovativeness, discomfort, and insecurity. The Technology Readiness Index (TRI) measures a person's tendency to accept and use technology to complete goals in their home life or at work. This study was conducted by distributing questionnaires to 348 students consisting of students of information systems and informatics engineering study programs. Data were obtained from a total population of 2689 students, 348 samples were obtained based on the Slovin formula with an error margin of 5%. Determination of the sample to determine the number of samples of each stratum in the population with proportionate stratified random sampling in the Information Systems study program of as many as 250 students and the Informatics Engineering study program of 98 students. Manual calculations and using applications show that computer students at Universitas Putra Indonesia “YPTK” Padang are very ready to adopt artificial intelligence technology with variable values ​​of optimism 93.27%, innovative 92.64%, discomfort 91.66%, and insecurity 88.73%. These results can be stated that the factors that influence the readiness to adopt artificial intelligence technology include optimism, innovative, discomfort, and insecurity with a median index value of all variables of 92.15%
Penerapan IoT pada Alat Temperature Monitoring System Cold Chain Box Vaccine Menggunakan Sensor DS18B20 Putra, Akmal Darman; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.589

Abstract

Unit Pelaksana Teknis (UPTD) Farmasi Dinas Kesehatan Kabupaten Siak  Office Pharmacy is fully responsible for maintaining the quality of the vaccine until the vaccine is distributed, the process of storing vaccines in the cold chain box has a problem, namely, it is not equipped with a real-time temperature monitoring device that can provide a warning to pharmacists if the cold chain box temperature rises due to internal or external damage. In addition to the problems mentioned, there is another problem, namely the temperature recording process is still done manually every 2 hours on the log sheet by pharmacists. Based on this, the purpose of this study is to develop technological innovation in the pharmaceutical field, namely by creating an IoT-based temperature monitoring tool that is integrated with the Telegram application and the Blynk IoT application. The research methods used in this study are the waterfall method, experiments, and UML modeling. The process of running this system begins when the sensor is inserted into the cold chain box then the sensor will send temperature data to the Blynk IoT application to be displayed in real-time. The performance of this technology works with the provision that if the temperature does not comply with the provisions, a warning will appear on the Blynk IoT and Telegram applications, the temperature data is then saved and will be used as a basis for making a report by the pharmacist. This research produces a temperature monitoring device using a DS18B20 temperature sensor and an ESP8266 microcontroller with an accuracy rate of more than 95% and this system can also provide real-time temperature data information and warnings via telegram. This research is expected to contribute to the pharmaceutical field as well as the benefits and convenience for pharmacists in monitoring temperature and recording temperature data
Penerapan Algoritme Advanced Encryption Standard (AES-128) untuk Mengamankan File Rekam Medis Pasien Tamin, Zulfiqar; Hendrik, Billy
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.592

Abstract

In the era of Industry 4.0, which describes the current trend of digital transformation and advancements in information systems, data and information security is of paramount importance for today's internet users. Data breaches involving patient medical records are among the most damaging incidents, necessitating enhanced security measures. This is the background for the development of this application. The problem analysis conducted by the researchers focuses on data file security. One effective method to enhance the security of your data or files is through encryption, specifically using the AES-128 cryptographic algorithm. Given the critical need for information security today, this research aims to implement cryptography using the AES-128 algorithm for encrypting and decrypting data in file formats. This study is implemented in a Python-based application, enabling users to protect sensitive data files. The data is encrypted, and only those with the application and the encryption key can decrypt or access the files. Several tests on this application have successfully encrypted and decrypted patient medical records in PDF format. The purpose of this application is to provide enhanced security for confidential file contents, ensuring that unauthorized parties cannot access them. The application was tested by encrypting and decrypting files in PDF format, with encryption and decryption times measured in seconds. The conclusion drawn from the AES-128 file encryption and decryption is that the larger the file size, the longer the encryption and decryption processes take
Penerapan Algortima K-Means Clustering untuk Optimalisasi Persediaan Liquid Vape Berdasarkan Data Penjualan Selfi Melisa; Defit, Sarjon; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.620

Abstract

Liquid vape is a liquid in an electronic cigarette (vape) device that contains a mixture of Propylene Glycol (PG), Vegetable Glycerin (VG), flavorings, and contains nicotine. As the use of vapes increases as an alternative to conventional cigarettes, efficient stock management becomes a challenge for vape shops to be able to meet customer needs without experiencing excess or shortage of inventory. Good stock management in a retail business is very important to maintain a balance between demand and product availability. This research aims to optimize liquid vape supplies by analyzing sales patterns. This research method is K-Means Clustering which includes several stages, namely determining the number of clusters, determining the centroid point randomly, calculating the closest distance between data and the centroid using the Euclidean method, grouping data into each cluster, updating the centroid until it is stable, and evaluating the results. The data used in the research is liquid vape sales data from June to November 2024 with a total of 68 product samples. Data processing was carried out manually and testing used RapidMiner software to measure the level of accuracy of the clustering results. The research results show that the K-Means Clustering algorithm is successful in grouping products into three categories: very popular, best selling, and not very popular. 51 products are in the low-selling category, 13 products are in the best-selling category, and 4 products are in the very best-selling category, with a Davies Bouldin value of 0.374%. The application of K-Means Clustering is effective in grouping products according to demand, helps determine the ideal stock amount, reduces the risk of product excesses or shortages, and increases operational efficiency
Penerapan Metode Simple Additive Weighting dan Fuzzy Logic dalam Menganalisa Mitigasi Risiko Rozakh, Muhammad; Siregar, Diffri; Nurcahyo, Gunadi Widi; Sovia, Rini; Rahman, Zumardi
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.621

Abstract

Risk management is a stage to identify and address risks affecting a system or project. The risk mitigation process takes time and must be carried out periodically to be effective. In the context of education, information technology plays an important role in increasing the speed and accuracy of decision-making, including in risk mitigation. This study aims to apply the Simple Additive Weighting (SAW) and Fuzzy Logic methods to provide recommendations for risk mitigation that must be prioritized in a university environment. This research method uses a combination of Simple Additive Weighting (SAW) and Fuzzy Logic. Starting with using SAW to determine the criteria, weights, and suitability ratings, followed by making a decision matrix and normalization. The ranking data is then processed with Fuzzy Logic to handle uncertainty and produce objective decisions through the formation of a rule-base, inference, and defuzzification. The research dataset consists of 50 risk records and criteria used in the risk mitigation process obtained from the University. The results of the study indicate that the application of DSS using the SAW and Fuzzy Logic methods provides recommendations for risk mitigation with the results of 1 data not recommended for risk mitigation, 8 data highly recommended, and 4 data recommended for mitigation. This study contributes to designing an effective decision support system, allowing university leaders to make appropriate risk mitigation decisions based on relevant and accurate data using the SAW and Fuzzy Logic methods
Prediksi Jumlah Kunjungan Pasien pada Bidan Praktik Mandiri dengan Jaringan Syaraf Tiruan Backpropagation Rifky, Muhammad; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.628

Abstract

Independent Midwives (BPM) are important in providing health services for mothers and children. One of the main challenges in managing BPM is the uncertain fluctuation in patient visits, making it difficult to plan resources, such as medical personnel, drug supplies, and other supporting facilities. If the number of patient visits cannot be predicted properly, the risk of shortages or excess resources becomes higher, which can impact operational efficiency and the quality of health services. Uncertainty in the number of patients can also affect financial planning and readiness to face a surge in visits. Based on this, this study aims to develop a prediction model for the number of patient visits using Artificial Neural Networks (ANN) with the Backpropagation method. The dataset uses data on the number of Antenatal Care (ANC) patient visits over the past three years. The results of the model evaluation were carried out based on the Mean Squared Error (MSE) value and the prediction accuracy level presented more than 94% accuracy level. The evaluation results also obtained an MSE value of 0.0023, and MAPE of 5.62% so that the results can be stated that the model prediction error is within acceptable limits. This predictive model can contribute to assisting BPM in resource planning, improving service efficiency, and strategic decision-making in managing health facilities
Penerapan Deep Learning Menggunakan Metode Convolutional Neural Network dan K-Means dalam Klasterisasi Citra Butiran Pasir Olivia, Ladyka Febby; Yuhandri, Y; Arlis, Syafri
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.629

Abstract

Agregat halus (pasir) merupakan bahan bangunan yang paling banyak digunakan dalam dunia konstruksi, sehingga kebutuhan pasir setiap harinya sangat banyak terutama di daerah perkotaan yang pembangunannya sangat pesat. Pasir berbentuk butiran - butiran yang memiliki tekstur berbeda untuk setiap jenisnya. Karakteristik pasir yang baik apat ditentukan melalui beberapa parameter, seperti segi kadar lumpur pasir, pemeriksaan kadar air nyata dan SSD, pemeriksaan gradasi, kadar air, zat organik, berat isi kondisi padat/gembur, daya serap, modulus kehalusan. faktor-faktor ini menjadi acuan dalam memilih pasir yang sesuai untuk berbagai kebutuhan konstruksi, termasuk plesteran dinding dan lantai. Parameter-parameter ini menjadi acuan dalam memilih pasir yang tepat untuk digunakan dalam berbagai kebutuhan konstruksi, termasuk plesteran dinding dan lantai. penelitian ini bertujuan untuk mengelompokkan kesesuaian antara butiran pasir untuk plesteran dinding atau lantai. Gambar dari citra butiran pasir memiliki nilai piksel yang banyak kerena terdiri dari tiga komponen warna yang mana red, green, blue. Sehingga membutuhkan teknik yang baik dalam menganalisa gambar ini. Metode yang digunakan dalam penelitian ini adalah Convulutional neural network (CNN) sebagai untuk mendeteksi dan mengekstraksi fitur butiran pasir, Convolutional Neural Network yang digunakan dalam penelitian ini adalah arsitektur resNet 50 sebagai memiliki kinerja tinggi dalam analisis citra.. Convolutional Neural Network memiliki arsitektur yang terinspirasi oleh struktur visual sistem manusia dan sangat efektif untuk tugas-tugas dalam ekstraksi gambar dan Metode K-means Clustering untuk menentukan pengelompokkan data ke dalam beberapa kelompok (klaster) sehingga data dalam satu klaster memiliki kemiripan tinggi sementara data antar klaster berbeda secara signifikan butiran pasir. Dataset yang diolah dalam penelitian ini bersumber di CV. Sumber Rezeki. Dataset terdiri 94 citra butiran pasir. Hasil penelitian menunjukkan bahwa pasir dapat diklasifikasikan ke dalam beberapa kategori mengelompokan seperti butiran bulat, butiran tajam, butiran tumpul, butiran tidak beraturan, butiran sub angular. Penelitian ini dapat menjadi acuan dalam menentukan kesesuaian butiran citra pasir yang cocok untuk lantai atau plesteran dinding dan membantu kontraktor memilih jenis pasir.
Penerapan Artificial Neural Network untuk Memprediksi Persediaan Obat Esensial Alfallah, Fadhly; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.630

Abstract

The availability of essential medicines is a fundamental factor in ensuring high-quality healthcare services, especially in primary healthcare facilities such as Puskesmas. Inefficient drug inventory management can lead to various issues, including drug shortages that disrupt medical services and overstocking that may result in waste due to expiration. An accurate prediction system is essential to support more effective and efficient drug inventory planning. This study aims to analyze historical drug usage patterns to generate more accurate predictions. The research methodology includes problem identification, data collection, preprocessing, ANN architecture design, implementation, and system evaluation. Historical drug usage data from previous years is used for training and testing, with a division of 70% for training and 30% for testing. The backpropagation algorithm is applied to optimize the model by adjusting parameters such as the number of neurons in the hidden layer, learning rate, and activation function. The study results show that the ANN model with a 12-12-1 architecture achieves a high prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.13% for paracetamol stock. The developed MATLAB application provides an interactive platform for users to input historical data and obtain dynamic stock predictions. This system implementation is expected to help Puskesmas manage drug inventory more effectively, reduce the risks of shortages and overstocking, and improve efficiency in essential drug distribution. This study contributes to the field of health informatics by demonstrating the effectiveness of ANN in drug inventory prediction. Future research may explore hybrid machine learning models or integrate external factors, such as seasonal disease patterns and community demand levels, to enhance predictive accuracy and adaptability.
Development of Signature Image Processing Using Shape and Texture Patterns Prihandoko; Rahmawati, Sri; Yuhandri, Muhammad Habib
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.635

Abstract

A signature is a sign in written form, a person's identity for whether a document is correct or not, commonly known as a Biometric system. The Biometric system is the most basic, crucial and considered a superb process for a signature in detecting a person's identification and security. Signature forgery is a fraud that often occurs, causing bigger and longer expenses. For reasons like these, a signature detection system must be able to quickly and accurately recognize genuine and dummy signatures. The purpose of this study was to present the original and dummy signature pattern recognition by grouping the original signature data. In this study, Image Segmentation was used to divide the image into several parts, the K-Means Clustering algorithm to group several parts according to the properties of each object, and Feature Extraction of Texture Patterns and Shape Patterns with Gray Level Co-Occurrence Matrix (GLCM) to obtain feature values such as Entropy, Energy, Homogeneity, Correlation, and Contrast which has resulted in a study to detect genuine and counterfeit signatures. Preliminary results show that the percentage of identification of the signature biometric system developed using Feature Extraction with signature shapes on texture patterns got an average similarity rate of: 92.74%, and signature shapes on shape patterns attained an average similarity rate of: 79.20%. Therefore, the texture extraction pattern can detect the degree of similarity between the original signature and the dummy signature with a higher percentage value compared to the shape extraction pattern. The proposed method can produce better accuracy

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