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Jurnal Teknologi dan Manajemen Informatika
Published by Universitas Merdeka Malang
ISSN : 16936604     EISSN : 25808044     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Jurnal Teknologi dan Manajemen Informatika (JTMI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Merdeka Malang. JTMI terbit 2 edisi per tahun pada Januari - Juni dan Juli - Desember dengan scope ilmu komputer yang mencakup teknologi informasi, sistem informasi, dan manajemen informatika.
Arjuna Subject : -
Articles 141 Documents
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Perancangan Aplikasi Sistem Informasi Distribusi Bantuan Bencana Alam dengan Memanfaatkan Metode Rational Unified Process Saepul Zaman
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6258

Abstract

Sukabumi Regency is a disaster-prone area with a high death toll compared to other areas, so there is often a commotion caused by the struggle for disaster aid. The information system used to assist the distribution of aid has not fully synergized with the development of information technology. To realize efficiency and effectiveness in logistics distribution, it is necessary to update information systems that can support the distribution of logistics assistance quickly, precisely, and as needed. Therefore, this study aims to design an information system application for the distribution of natural disaster assistance by utilizing a rational unified process that can provide solutions to problems that occur in the field, so that disaster victims will more easily get the information they need, and the distribution of aid from PMI can be accepted more easily. quickly by disaster victims.
Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing Firman Pradana Rachman; Handri Santoso
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6506

Abstract

Everyone has an opinion or opinion on a product, public figure, or government policy that is spread on social media. Opinion data processing is called sentiment analysis. In processing large opinion data, it is not enough to only use machine learning, but you can also use deep learning combined with NLP (Natural Language Processing) techniques. This study compares several deep learning models such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), and several variants to process sentiment analysis data from Amazon and Yelp product reviews.
Rekomendasi Supplement Learning Resources dalam E-Learning berbasis Fuzzy AHP Anastasia L Maukar; Fitri Marisa; Ahmad Farhan; Ibnu Khalim; Inayati Sa’adah; Mas ‘Edi; Muchamad Roni; Moch. Fajar
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6376

Abstract

Determining effective supporting learning sources in E-Learning is a complex problem because decisions are given to various criteria aimed at giving priority to the alternatives used. The Fuzzy-Analytic Hierarchy Process (Fuzzy AHP) approach is one of the procedures that can be a solution to this problem. This method can be used to determine decisions in the assessment of pre-existing criteria. From the calculation of the tests carried out, YouTube got the highest ranking, and then below it was followed by Google, Brainly, and finally Slide Player. From these calculations, it can be concluded that YouTube can be used as a supporting learning source in the E-Learning learning system.
Klasifikasi Tanaman Beringin (Ficus Bernjamina) berdasarkan Citra Daun Menggunakan Algoritma K-Nearest Neighbors Feri Wibowo; Agung Purwo Wicaksono; Lahan Adi Purwanto
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6758

Abstract

One of the problems faced when choosing a banyan, whether to be used as a shade plant, bonsai, or medicinal plant, is to identify the appropriate type of banyan. So research must be done to find out the desired type of banyan. One way that can be used to classify is with digital image processing technology, namely by extracting features or characteristics from digital images or images. The challenge is how to classify banyan plants based on leaf images using digital image processing. This study aims to design or design and compile a digital image processing program and the K-Nearest Neighbors (KNN) algorithm for the classification of the banyan species which can be used as a model for an automatic classification system using computer equipment. The results of the research on the process of testing the classification of ficus plants based on texture and shape characteristics on leaf images using the K-Nearest Neighbors algorithm can be concluded that the application has been successfully designed and built and can be used for the texture and shape feature extraction process and can be used for the classification process. From feature extraction, seven GLCM texture features are obtained, namely energy, entropy, contrast, homogeneity, IDM, variance, and dissimilarity, and 2 shape features, namely roundness, and compactness. The test results show a relatively low accuracy value of 56.25% with data on the number of images recognized according to the type of ficus as many as 18 and not recognized as many as 5 images
Implementasi Algoritma K-Means untuk Menentukan Persediaan Barang pada Poultry Shop Firman Nurdiyansyah; Ismail Akbar
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6377

Abstract

Maintaining inventory so that the goods do not get empty is one of the ways to maintain customer satisfaction. To do this, company management must be able to analyze which items are selling well and which items are not selling well, especially in the sales department. It is not easy to CV. Muria PS because it has a large number of items, so it takes a little computational technique to simplify the problem. The K-Means clustering algorithm was chosen to solve this problem because it can group the products sold and still available into several clusters. Of the three clusters formed, cluster 1 consists of two items, cluster 2 consists of 9 items, and the remaining 25 items are included in cluster 3. From these results, CV management can take advantage of this. Muria PS to increase inventory stock and sales strategy.
Analisis dan Perancangan Sistem Manajemen Inventaris Menggunakan Metode Fishbone Hijrah Hijrah; Maulidar Maulidar
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6501

Abstract

This research was conducted with a qualitative approach and conducted interviews, observations, and documentation review to obtain primary data and secondary data. The data obtained from the research stages will be analyzed using the fishbone method to obtain the root of the problem by looking at the causes and effects of these problems. After all stages of the analysis are carried out, it will be recommended applications that can help business companies engaged in internet services minimize losses due to excess stock. The recommended application is designed using information system design tools or methods that describe the system flow, data structure, and related relationships to facilitate the application and development of applications. From the results of the analysis of the research conducted, it can be concluded that the inventory design is needed so that an inventory application is designed according to the needs of the company
Pengembangan Aplikasi Android MVTE dengan Metode RAD Fikri Amrullah; Mardiana Andarwati; Galandaru Swalaganata; Hudan Eka Rosyadi
Jurnal Teknologi dan Manajemen Informatika Vol 7, No 2 (2021): Desember 2021
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v7i2.6754

Abstract

Deaf students discuss activities with other students, Deaf students need tools in participating in discussion activities, whereas so far, Deaf students have only relied on the ability to read the lips of their interlocutors. This research produces an assistive or assistive technology, namely the Android-based MVTe (Mobile Voice To Text) application where this application simply changes the voice in written form. This application was developed using the RAD (Rapid Application Development) method. The RAD method speeds up the work process due to its compact working process. The application trial was carried out on an IT expert, 5 deaf students, and 5 lecturers in the Information Technology Faculty, Universitas Merdeka Malang. The average test result is 81.3% with a feasible category for use.
Analisis Sentimen dan Klasifikasi Tweet Terkait Mutasi COVID-19 menggunakan Metode Naïve Bayes Classifier Dewandaru, Aryo; Wibowo, Jati Sasongko
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 1 (2022): Juni 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i1.6803

Abstract

Towards the end of 2019 in Wuhan City, China, a new type of Corona Virus was discovered which has the scientific name COVID-19 and is a type of virus that causes acute disorders in the human respiratory system. The spread of this virus is very fast and causes mutations of this virus to a more lethal stage than before. Thus, sentiment analysis is expected to be able to determine the trend of public assessment of the COVID-19 mutation. Naïve Bayes Classifier is a method used in research. This method can classify data or opinions into two sentiments, namely positive and negative. The research data comes from Twitter which is taken using the Twitter API with the keyword "covid mutation", for data processing several processes are carried out, namely sentiment classification, data cleaning, and preprocessing so that the final result is obtained. The test results from this study show that the Naïve Bayes Classifier method has an accuracy of 86.67% with an f1-score of 82.00% on positive sentiment and 89.00% on negative sentiment. Based on the results of the study, it can be concluded that the Naïve Bayes Classifier method can be used to analyze sentiment data from tweets about the COVID-19 mutation with an accuracy of 86.67%.
Analisis Perbandingan Algoritma Forecasting dalam Prediksi Harga Saham LQ45 PT Bank Mandiri Sekuritas (BMRI) Ramadhan, Viry Puspaning; Pamuji, Fandi Yulian
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 1 (2022): Juni 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i1.6092

Abstract

Economic development in Indonesia has slowed in recent years. This resulted in the movement of the index for several stocks listed on BEIm, especially LQ45 which also experienced increases and decreases. Therefore, it is necessary to analyze stock price movements so that the results of the analysis can be used by investors to make investment decisions. This study will apply several Forecasting algorithms such as Linear Regression and Neural Network to predict the stock price of LQ45 in the case study of Bank Mandiri Sekuritas (BMRI). By using four attributes, namely open, high, and low values as predictors and close as a class, this study focuses on determining the accuracy value, namely Root Mean Squared Error (RMSE) by optimizing parameter values. The test results obtained an RMSE value of 0.034 on the Neural Network method with the addition of a hidden layer and an RMSE value of 0.052 on the Linear Regression method with M5 Prime and Greedy Feature Selection with a min-tolerance value of 0.05.
Penerapan Aplikasi Android E-Payment dan Pemesanan Layanan Pujasera Subari, Subari; Ramadhan, Adrian
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 1 (2022): Juni 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i1.7780

Abstract

Pujasera or Multi-purpose Snack Center is an area where food and drinks are served or enjoyed by buyers which consist of many variations of sellers divided into outlets, booths, or stalls. In general, each of these outlets conducts transactions directly with buyers, both choosing orders, serving food, and paying. This raises several problems, including queue numbers that are no longer in order and table numbers that are not well-coordinated, and the occurrence of queues of buyers. The purpose of this study is to design an Android-based application with e-payment and ordering service features at the food court so that it is expected to minimize obstacles and errors that occur. The payment mechanism is also carried out virtually with the existence of e-payments to make it easier for buyers and store owners. The features provided start from the deposit and balance refunds, and other related services so that they don't have to queue and don't have to carry cash. This implemented system can provide convenience and comfort to buyers and store owners, the application works well and is integrated to facilitate ordering and payment services to be more transparent and more informative. The percentage of interpretation evaluation is categorized as very feasible, with the results of the evaluation of application users with an interpretation level of 90%.

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