OPERASI MORFOLOGI UNTUK MENDETEKSI KEBERADAAN BENDA TAJAM PADA CITRA X-RAY DI BANDARA

Pemeriksaan barang bawaan penumpang adalah hal yang mutlak dilakukan sebelum seseorang memasuki kabin pesawat untuk mengantisipasi masuknya benda berbahaya kedalam pesawat. Penentuan adanya benda berbahaya dalam tas bawaan penumpang dilakukan oleh petugas security  dengan mengamati monitor pada mesin x-ray bandara. Faktor kelelahan petugas sangat mempengaruhi tingkat akurasi pada proses pemeriksaan tersebut.  Sehingga pada penelitian ini dibuat perangkat lunak yang dapat diaplikasikan pada mesin x-ray guna membantu petugas security dalam menentukan adanya benda tajam yang diindikasikan sebagai barang berbahaya. Proses deteksi benda tajam diawali dengan segmentasi menggunakan Color base, proses filtering menggunakan Morfologi serta penentuan tajam atau tidaknya objek menggunakan Round Value

The Mapping of Potential Farms Commodities East Lombok Regency, NTB

This study aims to map the potential large livestock for (cows, buffaloes and horses) and reviewing development opportunities in East Lombok, West Nusa Tenggara Province. This study uses descriptive analysis approach combined with an analysis of primary data and secondary data. The collected data is processed using location analysis quetion, shift share analysis, development ratio analysis models and overlay analysis. LQ analysis results showed superior cows in District Suela, Sambelia, Labuan Haji, Pringgasela and Montong Gading. Buffaloes standout in Jerowaru, Sembelia, East Sakra, Keruak and Pringgabaya. Horses appear in Masbagik excellence, Suralaga, Sakra and Selong Keruak East. The area that give a competitive advantage for cows effects include Aikmel, Terara, Montong Gading, Wanasaba and Selong. A competitive buffalo advantage only includes Keruak District. A competitive horses advantage occurs in Sakra, East Sakra, West Sakra, Wanasaba and Pringgabaya. Cows have a high growth rate in Terara, Sukamulia, Aikmel, Sembelia, Sikur, Pringgasela, Masbagik, Pringgabaya, Wanasaba, Labuan Haji, Selong, Sembalun, Terara, Montong Gading, Suralaga and Suela. East Lombok does not have a region with a high growth rate for the buffalo. High growth horse rates occurred in west Sakra, Terara, Aikmel, and Pringgabaya. East Lombok is The potential area for development cattle are Terara, Montong Gading, Sikur, Pringgasela, Sukamulia, Suralaga, Selong, Labuan Haji, Swela and Sambelia. Buffalo less potential if developed in East Lombok. Aikmel and Terara good for the development of the horse. Keywords: Mapping, Large livestock, competitive advantag

Brain Tumor Classification in MRI Images Using En-CNN

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset

Klasifikasi Kondisi Keterampilan Motorik Halus Anak Usia Awal Sekolah dari Proses Menulis menggunakan Metode Fuzzy dan Random Forest

Klasifikasi keterampilan motorik halus (KMH) pada anak usia sekolah awal sangat penting untuk mendapatkan informasi tentang kesiapan sekolah. Di Indonesia, guru menilai KMH anak dengan mengamati langsung tulisan tangan atau tulisannya dibantu oleh psikolog pendidikan. Proses pengamatan ini tergantung pada persepsi pengamat sehingga dapat menimbulkan salah tafsir karena subjektifitas. Penelitian ini bertujuan untuk mengklasifikasikan KMH anak berdasarkan proses menulis huruf tegak bersambung dan aksara jawa. Penelitian ini mengusulkan model untuk mengeksplorasi data selama proses menulis huruf tegak bersambung dan aksara jawa dengan menggunakan papan digital (digitizer). Sistem merekam data secara langsung ketika anak-anak menulis huruf tegak bersambung dan aksara jawa. Peningkatan akurasi klasifikasi telah menjadi masalah penting dalam mesin pembelajaran terutama pada kumpulan data yang beragam yang berisi data outlier. Pada aliran data (data streaming) dari pembacaan sensor digitizer menghasilkan data yang besar dan memungkinkan terjadinya banyak data outlier. Hal tersebut membuat performa model mesin menurun. Oleh karena itu, diperlukan data yang bersih dari derau untuk mendapatkan akurasi yang baik dan untuk meningkatkan performa model mesin pembelajaran. Penelitian ini mengusulkan dua tahap untuk mendeteksi dan menghilangkan data outlier dengan menggunakan metode covariance estimator dan isolation forest sebagai pra-pemrosesan dalam proses klasifikasi untuk menentukan KMH. Dataset dihasilkan dari proses perekaman data secara langsung pada saat penulisan huruf tegak bersambung dengan menggunakan digitizer. Data termasuk posisi relatif stylus pada papan digitizer. Posisi x, posisi y, posisi z, dan nilai tekanan kemudian digunakan sebagai fitur dalam proses klasifikasi. Dalam proses observasi dan pencatatan, data yang dihasilkan sangat besar sehingga sebagian menghasilkan data outlier. Metode fuzzy dinilai sesuai untuk proses klasifikasi pada jumlah data set yang relative sedikit atau kurang dari 100 data. Dari hasil eksperimen yang telah diimplementasikan, tingkat akurasi pada proses klasifikasi KMH meningkat antara 0,5-1% dengan menggunakan pengklasifikasi Random Forest setelah pendeteksian dan penghilangan outlier dengan menggunakan estimator kovarians dan Random Forest. Tingkat akurasi tertinggi mencapai 98,05% dibandingkan dengan akurasi tanpa penghapusan outlier, yaitu 97,3%. ================================================================================================ Classification of fine motor skills (FMS) in early school-age children is essential to obtain information about school readiness. In Indonesia, teachers assess children's FMS by directly observing their handwriting or writing assisted by an educational psychologist. This observation process depends on the observer's perception so that it can lead to misinterpretation due to subjectivity. This study aims to classify children's FMS based on cursive writing process and javanese letter. This study proposes a model to explore data during the cursive writing process and javanese letter by using a digitizer. The system records the data directly when the children write cursively. The increase of the classification accuracy has become an important problem in machine learning especially in diverse dataset that contain the outlier data. In the streaming data from digitizer sensor readings could produce large data and allows a lot of noise to occur. It makes the performance of the machine learning model is disrupted or even decreased. Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model. This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as preprocessing in the classification process to determine FMS. The dataset was generated from the process of recording data directly during cursive writing by using a digitizer. The data included the relative position of the stylus on the digitizer board. x position, y position, z position, and pressure values are then used as features in the classification process. In the process of observation and recording, the generated data was very huge so some of them produce the outlier data. The fuzzy method is considered suitable for the classification process in the number of data sets that are relatively small or less than 100 data. From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest. The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is 97.3%

SISTEM PENGAMBIL DATA GAMBAR MENGGUNAKAN KAMERA SERIAL PADA MUATAN ROKET

KOMURINDO 2013 organized by the Directorate General of Higher Education and the Directorate of Research and Community Services together with National Institute of Aeronautics and Space gives 2 (two) theme of the competition and one of them is the High Rate Data Attitude Monitoring and Surveillance Payload. Payload or Payload Rocket is a compartment sensors telemetry data for monitoring purposes attitude and a camera for monitoring from space are arranged in a cylindrical tube measuring no more than 200mm in height and 100mm in diameter. In this final project will be discussed in accordance with the title of "Decision System Images Using Camera Serial Data on Rocket Payload" for which data will be sent to Ground Station via 433 MHz radio frequency transceiver modem YS1020U. The output of this camera series is data to hexadecimal format and not converted to images. This system can be combined with telemetry and monitoring systems for future rocket payload

KENDALI LAMPU RUMAH VIA SMS BERBASIS MIKROKONTROLER ATMEGA32L

In today's technological era in the development of all areas with extremely rapid technological base. Various inventions and technological developments have made a lot of changes for a wide range of growing order of life in the community. This helps facilitate human progress in completing the work that had been considered difficult and almost impossible to do. One of them is the field control with the discovery of the microcontroller as a tool automatic control.Through this thesis the author will use the microcontroller ATMega32L as one of the few products  to  control  the  light  output  ATMEL  home  via  SMS  with  the  method  on  /  off controller. The author uses the C programming language as programming for microcontroller ATMega32L.Control lights via sms is designed to turn on and turn off the lights with command via SMS. From the test results as a whole it can be concluded that the system can work well, it is seen from the results of testing that includes testing proces

OPTIMISASI PENGGUNAAN SMART PHONE PADA PEMESANAN MENU CAFE

Perancangan atau pembuatan Aplikasi Pemesanan Menu Pada Smart Cafe ini diharapkan akan mampu serta dapat diterapkan dengan membuat suatu aplikasi yang dapat mengubah cara pencatatan manual (konvensional) yang menggunakan media alat tulis dan kertas menjadi pencatatan dengan menggunakan suatu perangkat yang sudah ter komputerisasi sehingga aplikasi ini juga akan memudahkan proses pemesanan sehingga untuk menghindari terjadinya antrian panjang disaat melakukan proses pemesanan serta aplikasi ini dapat membantu memberikan data – data penjualan yang akurat sehinggan untuk mengurangi terjadinya kesalahan pada saat proses pemesanan