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Techno Nusa Mandiri : Journal of Computing and Information Technology
Published by Universitas Nusa Mandiri
ISSN : 19782136     EISSN : 2527676X     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal TECHNO Nusa Mandiri, merupakan Jurnal yang diterbitkan oleh Pusat Penelitian Pengabdian Masyarakat (PPPM) STMIK Nusa Mandiri Jakarta. Jurnal TECHNO Nusa Mandiri, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh dosen-dosen program studi Teknik Informatika.
Arjuna Subject : -
Articles 9 Documents
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Search results for , issue "Vol 19 No 1 (2022): TECHNO Period of March 2022" : 9 Documents clear
DECISION SUPPORT SYSTEM FOR PURCHASING OF MIRRORLESS CAMERA USING WEIGHTED PRODUCT METHOD Ayu Manik Martawiharjo; Melan Susanti; Mari Rahmawati
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.1722

Abstract

A mirrorless camera is a camera that does not have a mirror or a pentaprism with the size and workpiece of a compact camera, but has an equivalent capability to a DSLR camera. There are several mirrorless camera manufacturers widely known in the market, among others: Canon, Sony, Fujifilm, Nikon, Olympus, and Panasonic with the advantages of each manufacturer's specifications highlighted to enhance the attractiveness of consumers. With many types of mirrorless cameras in the market, many consumers are still confused in choosing which mirrorless camera is right and suited to their needs. Therefore, it takes a decision support system for the selection of mirrorless cameras using the weighted product method that can generate decisions about mirrorless cameras that comply with the selection of consumer criteria. The criteria used in this study are price, sensor size, megapixel, maximum ISO, and LCD. The results of this study show that the alternative mirrorless camera which has the highest value is the Olympus PEN E-PL9 camera with a value of 0.148.
ANALISIS KUALITAS LAYANAN KONSUMEN BERDASARKAN METODE SERVQUAL (SERVICE QUALITY) DAN ANALYTIC HIERARCHY PROCESS (AHP) Regina Puteri Laurichela; Cepi Cahyadi
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.2436

Abstract

Tujuan dari penelitian ini untuk mengidentifikasi atribut-atribut kualitas pelayanan, nilai selisih antara harapan dan persepsi (gap) dari masing – masing atribut dengan metode Servqual, dan prioritas rekomendasi perbaikan dengan metode Analytical Hierarchy Process. Penelitian ini adalah jenis penelitian deskriptif. Dalam pengumpulan data yang digunakan adalah dengan acak sederhana. Populasi dalam penelitian ini yaitu konsumen yang menggunakan jasa pada The Healthy Catering. Sampel yang diambil sebanyak 80 orang responden. Instrumen penelitian yang digunakan adalah identifikasi instrument servqual dan instrumen AHP. Metode pengumpulan data yang dilakukan adalah dengan observasi, studi pustaka, wawancara dan penyebaran kuesioner.Teknik analisis data yang akan dilakukan yaitu uji validitas, reliabilitas, metode Servqual (Service Quality) dan Analytic Hierarchy Process (AHP). Hasil penelitian menunjukkan bahwa 1) didapatkan 6 nilai gap tertinggi diantaranya E1 dengan nilai gap -0,375, R2 dengan nilai gap -0.350, R4 dengan nilai gap -0.338, E3 dengan nilai gap -0.275, R1 dengan nilai gap adalah -0,263, dan RV1 dengan nilai gap adalah -0,150 2) hasil perhitungan Servqual terbobot menunjukkan prioritas perbaikan untuk atribut dengan gap tinggi yaitu E1, R4, E3, RV2, R2, dan R1.
ANALYSIS SENTIMENT ON THE ACCEPTANCE OF CPNS 2021 ON TWITTER SOCIAL MEDIA USING TEXTBLOB Widi Astuti
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.2980

Abstract

The progress of information technology is growing rapidly along with the development of hardware and software developed by the world's largest companies. These advances have a significant impact on human life. Many jobs in human life use the help of technology. data mining technology, one of which is used in the field of research. In the process, data mining will extract valuable information by analyzing the existence of certain patterns or relationships from large data. Government agencies in Indonesia periodically organize the recruitment and selection of Candidates for Civil Servants (CPNS).
WEBSITE-BASED CERTIFICATE MANAGEMENT INFORMATION SYSTEM DESIGN IN TRAINING AND CONSULTANT DIVISION Daning Nur Sulistyowati; Muhammad Salbiyath
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3059

Abstract

Certification is a determination given by a professional organization to a person to show that a person has competence, able to do a specific job or task. The management of certificates at PT Markco International is still carried out in a simple manner using Microsoft Office applications, starting from managing personal data, passing person status, numbering and issuing certificates. The design of this Certificate Management Information System is intended to provide a better solution, namely a web-based information system that can convert the currently running manual system into a computerized system using a database. Waterfall software development method and data collection techniques by conducting observations and literature studies are applied in the development of this program. This design discusses the process of managing personal data, registration agencies and certificates. As a result, the certificate management program that has been created can manage related information such as data on persons, agencies, certificate categories, registrations and certificates. This proves that the Certificate Management Information System Design at PT Markco International can manage the certificate management process well.
SENTIMENT ANALYSIS ON TWITTER SOCIAL MEDIA ACCOUNTS: SHOPEECARE USING NAIVE BAYES, ADABOOST, AND SVM(EVOLUTION) ALGORITHM COMPARATIVE METHODS Rizky Nugraha Pratama; Ghina Amanda Kamila; Kresna Lazani T; Ilham Fauzi; Muhammad Reynaldo Oktaviano; Dedi Dwi Saputra
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3086

Abstract

The growth of Indonesian e-commerce is increasing along with the growth of internet use in Indonesia. In 2015, there were 92 million internet users in Indonesia. One of the popular online shopping platforms in Indonesia is Shopee. One of the services to see the response and reporting of problems from users, including shopeecare. shopeecare was created on the social media platform twitter to help facilitate communication between customers. with the amount of customer enthusiasm in tweeting and Retweeting existing content, we decided to research about Sentiment analysis on twitter social media accounts: Shopeecare uses the SMOTE NB, ADboost, and SVM comparison methods. From the data, the comparison results from the test experiments used the Smote + Naive Bayes, Smote + Naive Bayes + Adaboost, and Smote + SVM models. It is known that the Accuracy, Precision, AUC values of the Smote + SVM algorithm are higher than other algorithms, namely Accuracy 76.24%, Precision 75.65%, AUC 0.822. From the results of the algorithm comparison, it shows that the algorithm in determining the sentiment of the complaint and not complaint analysis is better than other algorithms.
COMPARING ALGORITHM FOR SENTIMENT ANALYSIS IN HEALTHCARE AND SOCIAL SECURITY AGENCY (BPJS KESEHATAN) ASYHARUDIN ASYHARUDIN; Novi Kusumawati; Ulfah Maspupah; Destia Sari R.F.; Amir Hamzah; Duwik Lukito; Dedi Dwi Saputra
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3167

Abstract

Twitter is a social media that can be used to express opinions and exchange information quickly with individuals and institutions such as the Healthcare and Social Security Agency (BPJS Kesehatan). Every word that a Twitter user utters has meaning and stellar emotion. This meaning can be reached through the process of sentiment analysis. Sentiment analysis is the process of understanding and classifying emotions such as positive or negative or complaining or not complaining. This study classifies tweet data related to BPJS Health services into two classifications, namely complain and no complain. Using 1,000 data from Twitter written on the BPJS Kesehatan Twitter account. In text mining, to build a classification, the transform case, tokenize, token filter by length, stemming and stopword techniques are used. Gataframework is used to assist the preprocessing and cleansing process. Rapidminer was used to create sentiment analysis in comparing three different classification methods of the Twitter data. The method used is the Nave Bayes algorithm and the Naïve Bayes algorithm with the addition of a Synthetic Minority Over-sampling Technique (SMOTE) feature and the Naïve Bayes algorithm with an SMOTE feature that is optimized with Adaboost. The Naïve Bayes algorithm is added with the SMOTE feature which is optimized with Adaboost to get the best value with an accuracy value of 69.11%, precision 69.93%, recall 68.89% and AUC 0.770
COMPARATIVE CLASSIFICATION OF LUNG X-RAY IMAGES WITH CONVOLUTIONAL NEURAL NETWORK, VGG16, DENSENET121 Muhammad Ilham Prasetya; Yuris Alkhalifi; Rifki Sadikin; Yan Rianto
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3010

Abstract

Lungs are one of the organs of the human body, and lung tissue will ultimately affect human abilities. The respiratory system exchanges oxygen and carbon dioxide in the blood. Problems that often occur are polluted air quality, many bacteria that attack the lungs, and lung disease can cause shortness of breath, mobility difficulties, and hypoxia, so that if not detected immediately it can cause death. In this regard, the aim of this study is to compare the classification of normal lungs with those of those suffering from Cardiomegaly. The preparation of this dataset is a form of contribution in improving the quality of the disease classification system on X-ray images. CNN, VGG 16 and DenseNet methods were chosen as classification methods to ensure performance and which method is the best for classifying Lung Diseases. It can be concluded that by using the DenseNet121 model, X-Ray images in this research dataset get an accuracy of 67.06%, for the VGG16 model it gets an accuracy of 68.94% and for the CNN model it gets the highest accuracy of 80.54%.
INTEGRATION OF FUZZY LOGIC METHOD AND COCOMO II ALGORITHM TO IMPROVE PREDICTION TIMELINESS AND SOFTWARE DEVELOPMENT COST Neneng Rachmalia Feta
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3037

Abstract

This study discusses improving the prediction of timeliness and cost of software development using the Constructive Cost Model II (COCOMO II) method and the application of Fuzzy Logic. And aims to obtain accurate time and cost prediction estimates on software development projects to obtain maximum cost results for a software development project. This study utilizes an adaptive fuzzy logic model to improve the timeliness of software development and cost estimates. Using the advantages of fuzzy set logic and producing accurate software attributes to increase the prediction of the time and price of software development. The fuzzy model uses the Two-D Gaussian Membership Function (2-D GMF) to make the software attributes more detailed in terms of the range of values. In COCOMO I, NASA98 data set; and four data projects from software companies in Indonesia were used to evaluate the proposed Fuzzy Logic COCOMO II, commonly known as FL-COCOMO II. Using the Mean of Magnitude of Relative Error (MMRE) and the Pred evaluation technique, the results showed that FL-COCOMO II produced less MMRE than COCOMO I, and the Pred value (25%) in Fuzzy-COCOMO II was higher than COCOMO I. In addition, FL-COCOMO II showed an 8.03% increase in prediction accuracy using MMRE compared to the original COCOMO. Using the advantages of Fuzzy Logic, such as accurate predictions, adaptation, and understanding can improve the accuracy of the timeliness and cost estimates of the software.
FINAL GRADE PREDICTION MODEL BASED ON STUDENT'S ALCOHOL CONSUMPTION rangga ramadhan saelan; Siti Masturoh; Taopik Hidayat; Siti Nurlela; Risca Lusiana Pratiwi; Muhammad Iqbal
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3056

Abstract

Untuk mengetahui pengaruh konsumsi alcohol dan dan beberapa faktor lainnya yang diperkirakan memiliki peran terhadap tingkat kinerja belajar remaja yang masih bersekolah, maka saat ini dilakukan penelitian terhadap data publik yang telah didapatkan dengan menggunakan teknik machine learning dengan melatih beberapa model untuk memprediksi nilai akhir sebagai acuan kinerja belajar pelajar. Dengan melatih beberapa model machine learning untuk memprediksi nilai tahun akhir dari bahasa portugal dengan melakukan metode komparatif membandingkan model Support Vector Regressor (SVR) dan Random Forest (RF) sehingga akan didapatkan model terbaik untuk memprediksi. Semua model memiliki hyperparameter yang harus disesuaikan. Untuk menyetel hyperparameter ini menggunakan menggunakan Cross Validation. Model terbaik untuk memprediksi nilai akhir G3 adalah Support Vector Regressor (SVR) dan Random Forest (RF), dan memiliki mean absolute error (MAE) masing-masing sekitar 2,24 dan 2,25. Melalui plot MAE, model SVR dan RF bekerja dengan baik. Tetapi, Dengan menganalisis distribusi kesalahan yang dibuat oleh kedua model, dapat disimpulkan bahwa SVR lebih seimbang, yaitu memiliki rasio yang lebih baik antara nilai yang diremehkan dan ditaksir terlalu tinggi, sementara RF berkinerja lebih baik pada outlier.

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