Jurnal Komputasi
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<![CDATA[Web Service Sistem Informasi Terpadu (SIMIPA) Menggunakan REST API ]]>
<![CDATA[Ardiansyah Ardiansyah]]>;
<![CDATA[Didik Kurniawan]]>;
<![CDATA[Dwi Sakethi]]>;
<![CDATA[Bustomi bustomi]]>;
<![CDATA[Bambang Hermanto]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2869
<![CDATA[SIMIPA web service is a service that is used to provide services in the form of information to the Android mobile apps, is the Student module, the Lecturer module, and the Parent module. The service provided uses the REST API as a bridge between the server and the client. The goal is that the REST API can be accessed on the Android mobile apps. The development of web services in this study uses the Scrum development method which has 26 product backlogs with 9 iterations of sprints. Each sprint has several stages, namely sprint planning, daily sprint, sprint review, and sprint retrospective. Web services developed using the PHP programming language and MySQL database. The result of this research is the SIMIPA REST API which can be accessed on the Android front-end. Android accesses the REST API via the GET and POST request methods using a URL that generates a JSON response. Based on several tests, the SIMIPA REST API URL is said to have been successfully developed and can be accessed on Android mobile apps according to the expected response. Data security using the REST API is focused on the POST method using JSON Web Token (JWT).]]>
<![CDATA[Iplementasi Fuzzy Pada Monitoring dan Kontrol Kualitas Air Tangki Pembibitan ikan Menggunakan LabView ]]>
<![CDATA[Andi Farmadi]]>;
<![CDATA[Dwi Kartini]]>;
<![CDATA[Muliadi Muliadi]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2864
<![CDATA[Abstract — Pada kolam pembibitan ikan, pengamatan kualitas air harus terus dilakukan secara berkala karena kondisi pembibitan ikan merupakan masa rawan kematian yang diakibatkan oleh perubahan kondisi lingkungan pembibitan, parameter yang paling berpengaruh dalam kelangsungan hidup ikan yaitu kondisi keasaman air (Ph), kekeruhan air (Turbidiy), oksigen terlarut dalam air (DO) dan suhu air. Parameter tersebut harus selalu dimonitor dan dikontrol untuk mencapai kestabilan lingkungan pembibitan sesuai yang diharapkan. Telah dibuat sistem monitoring dan kontrol terhadap parameter yang berpengaruh pada pembibitan ikan menggunakan sistem fuzzy inferensi. Pengukuran parameter lingkungan dilakukan menggunakan sensor kemudian nilai parameter tersebut disesuaikan dengan nilai fuzzifikasi yang telah dibuat hingga menghasilkan nilai defuzzifikasi, output dari defuzzyfikasi akan melakukan kontrol terhadap parameter tersebut untuk mencapai nilai kestabilan lingkungan air. Pengontrolan Ph dan kekeruhan air dilakukan dengan mengganti air hingga mencapai tinggkat ph dan kejernian air yang sesuai kondisi yang diharapkan, jumlah buangan air dapat dihitung menggunakan teorema fluida. Perhitungan fuzzy dan Pengembangan antarmuka monitoring dan kontrol dibangun menggunakan program berbasis grafik LabView.]]>
<![CDATA[IMPLEMENTASI METODE FUZZY LOGIC PADA SISTEM PAKAR PENDETEKSI KECERDASAN ANAK ]]>
<![CDATA[Devira Asha]]>;
<![CDATA[Muhamad Bahrul Ulum]]>;
<![CDATA[Yuli Asmi Rozali]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2861
<![CDATA[The multiple intelligences proposed by Dr. Howard Gardner in his book entitled Frame of Mind states that every child has unique and different characteristics and does not only focus on one intelligence. Parents who don't know how to improve their children's intelligence from all categories of intelligence, and only focus on language and logic intelligence, are considered as intelligent benchmarks for children. This expert system application aims to facilitate parents in knowing the level of intelligence of children so that children get the opportunity to develop at least from various dimensions such as musical, kinesthetic, intra and interpersonal, etc. This study applies calculations using the Mamdani fuzzy logic method. The results of this study are able to identify children's intelligence by designing a fuzzy expert system where input variables and output variables are generated from interviews with experts who produce 45 input variables and 9 outputs. For example, the calculation in this study uses 5 input variables and 1 output variable with the number of rules generated, namely 243 rules. The results obtained from the manual calculation of the fuzzy logic method are 50.68 with a fairly intelligent level of medium level. The system development method used in this research is the extreme programming method with the Laravel framework. This website-based expert system application is also a place for information on multiple intelligences where this information will be useful to find out how to improve children's intelligence from all categories of intelligence.]]>
<![CDATA[Klasifikasi Image Tumbuhan Obat (Keji Beling) Menggunakan Artificial Neural Network ]]>
<![CDATA[Rizky Prabowo]]>;
<![CDATA[Yunda Heningtyas]]>;
<![CDATA[machudor Yusman]]>;
<![CDATA[Muhammad Iqbal]]>;
<![CDATA[Ossy Dwi Endah Wulansari]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2868
<![CDATA[Indonesia sebagai salah satu negara tropis memiliki potensi hayati yang sangat besar. Salah satu potensi yang banyak dimiliki di Indonesia adalah tumbuhan obat. Salah satu cara mengenali jenis tumbuhan obat yaitu melalui bentuk fisik daun. Implementasi teknologi yang saat ini banyak berkembang, maka masyarakat akan banyak terbantu dalam mengenali tumbuhan obat disekitarnya. Gambar atau citra daun tanaman obat digunakan sebagai data yang mewakili jenis tumbuhan obat tertentu. Data yang digunakan merupakan data yang telah diberikan perlakuan khusus dalam pengambilan gambar atau citra. Praprosesing dilakukan pada data yang didapat sebagai langkah awal pemrosesan data. Pada penelitian ini, data yang digunakan merupakan data primer dengan total 2000 data. Data yang digunakan dibagi menjadi 1800 data latih, 160 data validasi dan 200 data testing. Data training digunakan untuk membentuk pola model. Model selanjutnya di validasi dengan menggunakan data validasi. Model dibangun menggunakan Convulution Neural Network yang merupakan varian dari Artificial Neural Network. Hasil akurasi penelitian 82.5% dengan kecepatan pembangunan model dengan 10 epoch adalah 139 second per epoch.]]>
<![CDATA[Evaluasi Sistem Informasi Media Online Menggunakan Metode Technology Acceptance Model (TAM) ]]>
<![CDATA[arief Ichwani]]>;
<![CDATA[Eva Milenia Surya Buana]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2863
<![CDATA[Abstract — Lensapena.id is a news provider website that has been established since 2019. However, many people do not know Lensapena.id and the public's lack of intention in using the Lensapena.id. Therefore, to find out the acceptance of Lensapena.id, it is necessary to evaluate the acceptance. The evaluation method used in this study is Technology Acceptance Model (TAM) with 5 variables, namely Perceived Ease Of Use, Perceived Usefulness, Attitude To the Act, Behavioral Intention and Actual System Usage. Data collection was done by questionnaires, observations and interviews. The sample assumption used in this study is 100 general public. The results showed that the level of acceptance of the Lensapena was worthy of Neutral which had a value of 4.4 and there were 4 accepted relationships, namely Attitude Toward The Act on Behavioral Intention, Behavioral Intention on Actual System Usage, Perceived Ease Of Use on Attitude Toward The Act, Perceived Ease Of Use on Perceived Usefulness and 2 rejected relationships, namely Perceived Usefulness on Attitude Toward The Act and Perceived Usefulness on Behavioral Intention. Based on the research conducted, several recommendations are given to increase acceptance on the Lensapena.id.]]>
<![CDATA[Sistem Analisis Rekomendasi Saham Pada Indeks LQ45 Menggunakan Indikator Moving Average Convergence Divergence (MACD) dan Relative Strength Index (RSI) ]]>
<![CDATA[Kalista Setiawan]]>;
<![CDATA[Tristiyanto Tristiyanto]]>;
<![CDATA[Anie Rose Irawati]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2870
<![CDATA[The LQ45 stock index is a stock exchange that is in great demand by investors in Indonesia. Therefore, the movement of the stock price is needed by investors to see the investment business prospects. However, stock price movements in a certain period are very volatile. In this case, investors need a monitoring system to assist their investment decisions. One of the most popular analyzes of stock price movements is technical analysis. In this study, the system is a website that can be accessed by the public. It provides technical analysis indicators of Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and Candlestick charts. The system uses the AnyChart JS component as a modern JavaScript library chart. The waterfall method is used to assist develop this system. The system consists of two sources API, namely Rakuten Rapid (APIdojo.net) and finnhub.io with a query provider that can connect the Yahoo Finance API. The results of stock recommendations in this system come from determining the largest value for several recommendations generated by the finnhub.io based on basic technical and aggregate analysis (MACD, RSI, and Moving Average). Thus, this recommendation is only a suggestion and cannot be used as an absolute reference.]]>
<![CDATA[AdaBoost Classifier untuk Klasifikasi Tanaman Jarak Pagar ]]>
<![CDATA[Triando Hamonangan Saragih]]>;
<![CDATA[Muliadi Muliadi]]>;
<![CDATA[Mohammad Reza Faisal]]>;
<![CDATA[Muhammad Al Ichsan Nur Rizqi Said]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2865
<![CDATA[Tanaman Jarak Pagar merupakan tanaman multi fungsi yang memiliki banyak kegunaan di kehidupan sehari-hari, baik itu untuk pengobatan, kecantikan hingga pengganti bahan bakar biodiesel. Penyakit yang menyerang tanaman jarak pagar dapat menurunkan kualitas yang dihasilkan jarak pagar. Minimnya pengetahuan petani dan sedikitnya jumlah pakar yang memahami tentang jarak pagar menjadi masalah yang harus diselesaikan. Pengguanaan sistem pakar menjadi solusi yang bisa ditawarkan. AdaBoost Classifier pada sistem pakar dapat digunakan sebagai mengklasifikasikan penyakit tanaman jarak pagar. Hasil yang diperoleh dari penelitian ini yaitu didapat akurasi rata-rata sebesar 50% dan maksimal terbaik sebesar 53,01% pada jumlah fold sebanyak 2. Hasil pada penelitian ini lebih baik dibanding penelitian sebelumnya, tetapi tidak bisa memberikan hasil yang maksimal. Jumlah data tiap kelas menjadi perrmalasahan mengapa hasil pada AdaBoost kurang maksimal dan harus diselesaikan pada penelitian selanjutnya.]]>
<![CDATA[ANALISIS CELAH KEAMANAN APLIKASI WEB E-LEARNING UNIVERSITAS ABC DENGAN VULNERABILITY ASSESMENT ]]>
<![CDATA[Arief Budiman]]>;
<![CDATA[Syaiful Ahdan]]>;
<![CDATA[Muhammad Aziz]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2800
<![CDATA[The development of information and communication technology today brings convenience to human life. One of the things that is growing quite rapidly is a web-based application. The proliferation of web-based applications is a challenge for web-based application developers in developing security aspects. Vulnerability Assessment of the E-Learning web application aims to detect vulnerabilities, describe vulnerabilities, assess vulnerabilities based on the Common Vulnerability Scoring System, and provide solutions. The research stages used were the Vulnerability Assessment and Penetration Testing Life Cycle. In looking for vulnerabilities in this study using the Home version of the Nessus Vulnerability Scanning. Based on the results of the vulnerability scanning, it was found low vulnerability, medium vulnerability, high vulnerability, and critical vulnerability. Each vulnerability certainly has a different impact on vulnerability, but on a critical vulnerability, namely the Elasticsearch Transport Protocol Unspecified Remote Code Execution has the most serious impact with a base score of 9.8, so the overall risk level on the Web E-Learning application is High. So it can be concluded that the E-Learning Web application at ABC University is said to be vulnerable, because it has a serious impact that affects Confidentiality, Integrity, and Availability of the E-Learning web application through its vulnerabilities. Therefore, ABC University must immediately make improvements and evaluations of the security of the E-Learning Web Application so that the risk of vulnerability in the E-Learning Web Application can be reduced.]]>
<![CDATA[RANCANG BANGUN SISTEM MONITORING KUALITAS AIR PADA BUDIDAYA IKAN HIAS AIR TAWAR BERBASIS IOT (INTERNET OF THINGS) ]]>
<![CDATA[WAHYU DEWANTORO]]>;
<![CDATA[Muhamad Bahrul Ulum]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2858
<![CDATA[Ikan hias adalah hasil budidaya yang sangat diminati oleh berbagai lapisan masyarakat baik di dalam maupun di mancanegara. Di Indonesia pada tahun 2012 berkembang sebesar 115,16 persen. Ikan hias memiliki kapasitas dalam berbagai kondisi yang sangat dipengaruhi oleh nilai batas alami, misalnya, keadaan air, suhu, dan tingkat.keasaman (pH/ Potensial of Hidrogen), kekeruhan air. Batasan-batasan ini harus secara konsisten diamati untuk kelangsungan hidupnya. Karena permasalahan tersebut diperlukan sistem untuk mengawasi kualitas air dan dapat diakses di mana saja dengan menggunakan IoT (Internet of Things). Dalam membangun sistem pemeriksaan kualitas air untuk pengembangan ikan hias air tawar berbasis IoT menggunakan PIECES dan metode prototype pengembangan dan perencanaan dengan diagram Unified Modelling Language. Sensor yang digunakan adalah sensor pH-4502C untuk mengukur tingkat pH/tinggat keasaman pada air kolam atau aquarium, sensor DS18B20 untuk mengukur suhu pada air kolam atau aquarium, sensor turbidity SENO189 untuk mengukur tingkat kekeruhan pada air kolam atau aquarium, buzzer yang berfungsi sebagai aksi sistem ketika air pada kolam keruh, dan juga relay berfungsi sebagai aksi untuk menyalakan pompa pH naik atau pH turun ketika nilai pH di kurang baik, serta mikrokontroler ESP32 yang digunakan untuk mengolah data sensor dan mengirim data sensor melalui jaringan wireless, data yang dikirim oleh ESP32 dapat di monitoring melalui aplikasi android.]]>
<![CDATA[DENOISING CITRA TULISAN TANGAN AKSARA LAMPUNG MENGGUNAKAN CONVOLUTIONAL AUTOENCODER ]]>
<![CDATA[Saniati Saniati]]>;
<![CDATA[Verdy Haris Munandar]]>;
<![CDATA[Rikendry Rikendry]]>;
<![CDATA[Maulana Aziz Assuja]]>
<![CDATA[ Jurnal Komputasi Vol 9, No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Lampung ]]>
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DOI: 10.23960/komputasi.v9i2.2895
<![CDATA[The history of a nation or a region is stored in written historical documents using paper, walls, stone, metal, and other media. Efforts to maintain cultural heritage, including these documents, are still being carried out. One of the efforts is to save it in digital form or photos, but it is possible for the obtained images become noisy images. Many factors caused an image to have noise including outdated documents, image results that are influenced by camera lenses, lighting that is not ideal, ect. Noise can affect the information in the image, it is needed to made improvements so that the quality image results can be used for other purposes, both as digital documents and further research such as written recognition. In this research, the Convolutional Autoencoder approach is used to study noise from training data and reconstruct the image into a noise-free image. The noise used in this study will be created using the Gaussian, Salt & Pepper, and Spackle methods on the image of the Lampung script. The hyperparameters on the Convolution Encoder that were tested produced good performance for the model used by achieving low loss of 0.1453 and vall_loss of 0.1504 and also could reduce noise contained in images with various noise types and intensities.]]>
