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Smatika Jurnal : STIKI Informatika Jurnal
Published by Universitas Bhinneka Nusantara
ISSN : 20870256     EISSN : 25806939     DOI : https://doi.org/10.32664/smatika
Core Subject : Science,
Computer Science & IT
SMATIKA: STIKI Informatika Jurnal is a journal published by Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM) of Universitas Bhinneka Nusantara Malang. The scope of this journal in the field of Computer Science, Information Systems, and Information Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 258 Documents
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Sistem Rekomendasi Produk Konveksi Pada Deem Clothing Dengan Metode Knowledge Based Alwi Irham Hanafi; Agustina Srirahayu; Anisatul Farida
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1338

Abstract

Knowledge-based recommendation systems have become a crucial solution in assisting customers to select products that match their preferences, particularly in the garment industry. This study aims to develop a knowledge-based recommendation system for Deem Clothing's garment products, capable of addressing the challenges of direct product consultation. The study utilizes data obtained through interviews with the owner of Deem Clothing, direct business observations, and an analysis of the product catalog data. The method involves seven product criteria constraints: product type, material type, pattern, design details, color, additional accessories, and sleeve type. The recommendation process is conducted by implementing a simple constraint-based algorithm to generate product similarity scores and rank them from highest to lowest. The results indicate that the developed recommendation system can effectively and efficiently provide product recommendations that align with customer preferences. The conclusion of this study is that knowledge-based recommendation systems can reduce customer dependence on direct consultations, enhance the shopping experience, and optimize the sales process of garment products. The implications of this research for the field of knowledge are that knowledge-based approaches in recommendation systems can be widely applied across various industries to improve customer interaction and satisfaction.
Sistem Informasi Persediaan Barang Berbasis Web Di Toko Jam Sumber Terang Jember Titasari Rahmawati; Hermawan Andika
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1342

Abstract

The rapid development of technology makes the business world more efficient and effective, so information becomes crucial for business success. The implementation of information presentation for business purposes has been widely realized in the form of information systems that are indispensable for managing and understanding business processes, especially in complex environments such as the Clock Shop Sumber Terang Jember, where employees are assigned to check goods manually with paper media, but the ability to data is not on time. This method also causes problems such as delayed data transmission and delays in handling the recording of large quantities of goods. Another problem that arises is that the data search process will take a long time. To answer these challenges, a website system will be developed to provide comprehensive information about warehouse inventory. This system is designed to overcome the problem of delay and inefficiency caused by manual recording. This information system uses HTML, PHP, Bootstrap CSS Framework, and MySQL technologies. The main features provided include login, management of item data, item categories, users, checklists, approval of item checking data, and report generation. System implementation includes hardware and software specifications, as well as a user interface designed to simplify the inventory management process. The conclusion of this research shows that the information system developed can help check goods more quickly and efficiently. Suggestions for further development include the addition of goods inspection features and stock management.
Implementasi Convolutional Neural Network (CNN) Untuk Mendeteksi Ujaran Kebencian Dan Emosi Di Twitter Nanda Mujahidah Andini; Yulian Findawati; Ika Ratna Indra Astutik; Ade Eviyanti
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1346

Abstract

The research aims to develop an accurate and efficient hate speech detection model on Twitter's social media platform by leveraging the power of the Convolutional Neural Network. (CNN). The focus of this research is on identifying hate speeches that are loaded with negative sentiment, especially those related to racial, religious, and sexual orientation issues in the context of the Indonesian language. The research process involved collecting relevant Twitter datasets, preprocessing text to clear and compile data, and word representation using Word2Vec to capture contextual meanings. Specifically designed CNN models are then trained on that dataset. CNN's advantages in automatically extracting semantic features from text, coupled with the use of Word2Vec, allow the model to have high accuracy, which is 87%-99% for emotional assessment and 99% for hate speech assessment. This makes the model very effective in detecting subtle patterns in language that indicate the presence of hate speech. This research has made a significant contribution to the development of a better content moderation system on social media. With its ability to detect hate speech in real time, the model can help create a safer and more inclusive online environment. However, this research still has some limitations, such as limited data set size and variations of hate speech that are not fully represented. Therefore, further research is needed to overcome these limitations and improve the performance of the model.
Komparasi Metode K-Nearest Neighbor dan Naïve Bayes untuk Mengklasifikasi Resiko Diabetes Di Posbindu Desa Bulupitu Rizki Alifia Safitri; Rahmatina Hidayati
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1350

Abstract

Diabetes mellitus is one of the fastest-growing health problems in the 21st century. One of the causes is the lack of public awareness for regular health check-ups, while the lifestyle being led is quite unhealthy. Hemoglobin A1c (HbA1c) examination is highly recommended to detect diabetes. However, this service is not yet available at Posbindu in Bulupitu Village. Therefore, another approach is needed to detect the risk of diabetes early, namely through data mining. The data mining methods used in this research are the Naïve Bayes and kNN classification methods. The variables to determine the risk of diabetes include gender, age, family history of diabetes, frequent urination, Body Mass Index (BMI), blood sugar levels, and diabetes risk output. The division of testing and training datasets uses cross-validation and ratio (60:40, 70:30, 80:20, and 90:10). The best accuracy of the Naïve Bayes method was obtained by dividing the dataset using k-fold cross-validation with k=2, achieving 96.1%. In the kNN method, the best results were obtained from the 80:20 dataset ratio. Manhattan distance was found to be the best distance calculation in this study compared to Euclidean distance and Chebyshev distance.
Rancang Bangun Prototipe Sistem Rekomendasi Kualitas Air Untuk Budidaya Ikan Nila Mochamad Subianto; Ernanda Kusuma Wardhana
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1352

Abstract

In fish farming, specific processes are required for fish intended for food or ornamental purposes. In addition to food and weather, water quality must also be considered. Water quality parameters include ammonia content, water temperature, pH, turbidity, and Total Dissolved Solids (TDS). Poor water quality can result in the presence of toxic compounds, leftover feed, organic materials, and substances that cause diseases in fish. Conversely, good water quality can reduce water turbidity, allowing sufficient sunlight penetration and potentially increasing fish productivity. This study discusses a water quality detection device using five sensors: a temperature sensor, pH sensor, ammonia sensor, TDS sensor, and turbidity sensor, all connected to an Arduino Nano ATmega-328 to read the sensor data. Testing was conducted under five different water conditions: tilapia pond water, clean water, tilapia pond water mixed with clean water, catfish pond water, and tilapia pond water mixed with catfish pond water. The standard deviation for temperature, pH, and ammonia for all water conditions was less than 0.1. The standard deviation for TDS in catfish pond water and tilapia pond water mixed with catfish pond water was less than 1.0, and the turbidity values were below 7.
Implementasi Algoritma K-Nearest Neighbors (KNN) Untuk Prediksi Gizi Buruk Dian Hasna Ramadhani; Jumadi Jumadi; Gitarja Sandi
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1360

Abstract

Malnutrition is a serious problem in developing countries, caused by a lack of food intake containing essential substances such as protein and energy. The implementation of machine learning algorithms can provide an accurate diagnosis of malnutrition health conditions in toddlers, facilitating early detection and appropriate interventions. The purpose of this study is to determine the performance of the K-Nearest Neighbors (KNN) algorithm in predicting malnutrition based on clinical characteristics possessed by toddlers. The data used are clinical characteristics of malnutrition sourced from a nutritionist. From the research results, the most optimal accuracy value in predicting malnutrition is 87%. With the existing dataset, it can be proven that the K-Nearest Neighbors (KNN) algorithm is able to classify malnutrition into 2 conditions, namely marasmus and kwashiorkor.
Inovasi Aplikasi Sistem Informasi Laundry Sepatu Dengan Menggunakan Metode Waterfall Nahriyan Zidan Bahar Rizqi; Sumarno Sumarno; Ade Eviyanti; Nuril Lutvi Azizah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1366

Abstract

The method of checking shoe clothing carried out by most clients is still conventional. Clients still got to check with the shoe clothing put to begin with. With this strategy, there are still a few issues that happen, particularly the time and vitality went through in carrying out the checking prepare gets to be incapable and wasteful. This inquire about points to plan and construct a web-based data framework utilizing the Waterfall strategy. The data framework that has been built can fathom and give development for issues that happen with respect to checking shoes that have not been or have been prepared rapidly and make it less demanding for clients to get data almost the shoe clothing process via the net. The data framework is outlined based on the stages contained within the Waterfall strategy. In the mean time, the data framework improvement handle employments the Visual Code Studio application and MySQL database.
Pengujian Black Box Menggunakan Metode Equivelence Partitions dan State Transition Pada Aplikasi Angrem RSUD Campurdarat Muhammad Taufikurrohman; Ilyas Nuryasin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1367

Abstract

Application testing is important to ensure that the application can function properly without any bugs or errors from the application system. In this study, testing of the Angrem RSUD Campurdarat application needs to be done to ensure that the application can function properly without any bugs or errors when the application is used. This test uses two methods of black box testing, namely equivalence partitions and state transitions. The use of these two methods is necessary because they have different focuses. Equivalence partitions test the data input section from the user while state transitions test the flow or transition of the application. Equivalence partitions testing divides the input space into several partitions so that it can reduce the number of test cases. Based on the research that has been conducted on the Angrem RSUD Campurdarat application using the equivalence partitions and state transition methods, the test results show quite good application performance with some parts that need improvement. The results obtained are that in the equivalence partitions method from 44 test cases, there are 37 successful test cases and 7 failed test cases while in the state transition method there are 16 page transitions tested and all tests in this method are successful.
Deteksi Emosi Pada Citra Wajah Dengan Deep Learning Sebagai Alat Pendukung Terapi Bagi Pengidap Alexithymia Alfin Yogi Setyawan; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1368

Abstract

Alexithymia is a condition characterized by difficulty in identifying and verbally expressing emotions, which can hinder an individual's ability to understand and manage their emotions. This study aims to implement and develop a model that can detect emotions using the MobileNetV2 architecture for therapy purposes for individuals experiencing alexithymia. The method uses the FER-2013 dataset, which consists of 35,887 grayscale facial images in 7 emotion categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. Using a deep learning approach based on CRISP-DM, the research begins with normalization and data augmentation to improve the model's resilience to image variations. The developed model achieved a training accuracy of 67.7% and a validation accuracy of 65.3%, demonstrating significant capability in recognizing and classifying emotions from facial images. Evaluation using a confusion matrix showed that the model produced a precision of 64.9%, a recall of 65.4%, and an F1-score of 63.7% for each emotion class. This research implies the potential for developing systems that can support psychological therapy, especially to help individuals with alexithymia understand and manage their emotions through facial expression analysis, providing technology sensitive to emotional expressions.
Implementasi Data Mining menggunakan Metode Naïve Bayes pada Persediaan Obatobatan di RSUD Kayuagung Nurul Huda; Kevin Olivia Indri Putri
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1383

Abstract

Hospitals today have widely adopted inventory systems for managing drug supplies, which involve the process of big data analysis to identify relevant patterns, relationships, and trends in the drug inventory data. The goal is to optimize stock management, reduce waste, and ensure the availability of the right medications at the right time. Effective and efficient drug inventory management is a crucial aspect of hospital operations, ensuring timely availability of medications and minimizing the risks of shortages or overstocking. In this study, the Naïve Bayes method was chosen for its ability to handle large and complex datasets and produce accurate predictions. The research process involved several stages: problem identification, problem formulation, data collection, model classification creation, application development, model implementation, research testing, and report preparation. The findings of this study demonstrate the significant potential of data mining in inventory management within the healthcare sector.

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