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All Journal JOINT (Journal of Information Technology Jurnal Teknologi Informasi dan Komunikasi International Journal of Community Service Brilliance: Research of Artificial Intelligence
Suhendri
Lecturer Of Politeknik Jatiluhur, Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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METODE GRAYSCALE CO-OCCURRENCE MATRIX (GLCM) UNTUK KLASIFIKASI JENIS DAUN JAMBU AIR MENGGUNAKAN ALGORITMA NEURAL NETWORK Suhendri, Suhendri; Rahayu, Putri
JOINT (Journal of Information Technology) Vol 1 No 1 (2019): JoinT (Journal of Information Technology)
Publisher : Bagian Penelitian, Pengabdian Masyarakat & Pusat Inovasi STMIK "AMIKBANDUNG"

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1614.823 KB) | DOI: 10.47292/joint.v1i1.4

Abstract

Warna dan bentuk daun masing-masing tanaman jambu air berbeda sehingga dapat ditemukan suatu tekstur tertentu untuk mengklasifikasikannya. Penelitian ini menggunakan pengenalan tekstur suatu citra daun untuk diklasifikasi.Daun yang digunakan yaitu tiga jenis daun jambu air, Jambu Bunton 3 Hijau, Jambu Bol dan Jambu Citra. Proses ekstraksi fitur yang digunakan sebuah metode adalah Gray Level Co-Occurrence Matrix (GLCM) dengan tool Matlab. GLCM tersebut digunakan untuk mengambil nilai atribut citra atau nilai matrix. Penelitian ini menggunakan Algoritma Neural Network dengan tool RapidMiner. Salah satu alternatif solusi untuk mengatasi masalah diatas yaitu dengan cara pengklasifikasian jenis daun jambu air dengan melihat karakteristik daun jambu air tersebut. Daun merupakan salah satu karakteristik tanaman yang mudah dikenali. Proses pengklasifikasian yaitu untuk menghasilkan suatu nilai akurasi yang baik terhadap daun jambu bunton 3 hijau, jambu bol, dan jambu citra. Hasil penelitian ini menunjukkan bahwa tingkat akurasi pada daun jambu bol adalah 81,25%, daun bunton 3 Hijau 75%, dan daun citra 80% dan total nilai akurasi keseluruhan 78.89%. Dengan demikian hasil diatas menunjukan bahwa nilai akurasi yang dihasilkan menunjukan penelitian tiga jenis daun jambu air telah terklasifikasi dan layak untuk diteliti.
PENINGKATAN SISTEM KEAMANAN PARKIR DENGAN TEKNOLOGI ARTIFICIAL INTELLIGENCE IMAGING Wahyu, Ari Purno; Suhendri, Suhendri; Heryono, Heri
JOINT (Journal of Information Technology) Vol 1 No 2 (2019): JoinT: Journal of Information Technology
Publisher : Bagian Penelitian, Pengabdian Masyarakat & Pusat Inovasi STMIK "AMIKBANDUNG"

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (622.152 KB) | DOI: 10.47292/joint.v1i2.9

Abstract

Tempat parkir merupakan sebuah fasilitas umum yang biasa  tersedia pada sebuah instansi atau kantor yang berfungsi untuk menyimpan kendaraan sementara disaat kita bekerja atau melakukan aktifitas lainnya, kendarran yang masuk ke area parkir kemungkinan puluhan bahkan ribuan, oleh kerena itu diperlukan sebuah sistem pengaturan dan manajemen area parkir, pengaturan tersebut bisa berupa  prosedur parkir bahkan sistem pendukung lainnya seperti sarana dan prasaran parkir yang memadai, fungsi lain  dari pembuatan dan pengembangan sistem parkir pada umumnya untuk memberikan keamanan dan kenyaman, sehingga kondisi kendaraan akan tertata dengan baik dilihat dari segi penempatan kendaran serta jaminan  kemanan dan bisa dimanfaatkan selama 24 jam. Kendala yang dihadapi saat ini adalah jumlah kendaraan yang semakin bertambah sehingga diperlukan area atau space parkir yang lebih luas, lambatnya pendataan kendaraan karena teknologi yang digunakan masih dilakukan validasi plat kendaraan secara manual, masalah lain yang dihadapi  adalah pada penempatan area yang luas, keterbatasan ini berdasarkan pada jumlah petugas parkir dilapangan sangat terbatas sehingga diperlukan tenaga dan waktu yang ektra untuk mengatur dan mengecek kendaraan yang sudah masuk di area parkir. Masalah tersebut bisa diatasi dengan penggunaan teknik image processing dan OCR algoritma, teknik ini sudah banyak diimplementasikan dibeberapa  negara maju yang digunakan untuk manjemen sistem parkirnya,  image processing digunakan untuk mendata dan mengawasi jumlah kendaraan yang ada diarea dengan melakukan pembacaan dengan cara scanning plat nomor kendaraan, teknik scanning bisa bekerja dengan menggunakan teknik OCR (Optical Character recognition), data dari sebuah image plat nomer kendaraan dikonversi kedalam bentuk teks atau angka dan  bisa langsung disimpan pada sebuah database, data dari  plat kendaraan yang telah disimpan kemudian dicocokan dengan foto kendaraan,  dengan bantuan sistem bisa dintegrasikan  dengan kamera sehingga pengawasan area parkir bisa dilakukan secara langsung dalam waktu yang lama, sistem  mampu  menampilkan data secara visual plat kendaraan dan foto kendaraan tersebut.
SISTEM INFORMASI PEMERIKSAAN JALUR KERETA API MENGGUNAKAN DRONE DAN TEKNIK IMAGE PROCESSING Mardiana, Siti; Hamdani, Dani; Chaniago, M Benny; Wahyu, Ari Purno; Heryono, Heri; Suhendri, Suhendri
JOINT (Journal of Information Technology) Vol 2 No 1 (2020): JOINT: Journal of Information Technology
Publisher : Bagian Penelitian, Pengabdian Masyarakat & Pusat Inovasi STMIK "AMIKBANDUNG"

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (758.482 KB) | DOI: 10.47292/joint.v2i1.002

Abstract

Kereta merupakan moda transportasi utama yang sering kita gunakan, kereta sendiri bisa dimanfaatkan sebagai alat untuk pegiriman barang dan memobilisasi penumpang, transportasi ini sangat unik yang dan memiliki jalur tersendiri berupa rangakaian besi baja melintang hingga beratus kilometer, struktur bantalan kereta api saat ini ada yang menggunakan beton dan kayu, jalur kereta tersebut sangat vital dan merupakan sarana pendukung penting. Proses monitoring jalur kereta api sendiri sangat ini komplek dan rumit diperlukan waktu yang lama, cara sebelumnya bersifat sederhana dan konvesional dengan menyusuri jalur kereta secara manual atau menggunakan alat pengukur geometri yang dipasang direl atau dikenal juga dengan nama railpod, railpod akan menyusuri rel dan akan memberikan report jika terdapat jalur kereta apa yang rusak, patah atau bergeser, pada penelitian ini akan dibuat sistem monitoring berbasis image dengan memanfaatkan drone sebagai alat pemantau jalur rel, cara lain adalah dengan pengambilan gambar bisa menggunakan data satelit yang akan memberikan informasi yang jelas tentang kondisi jalan sebelum dilalui oleh kereta api, sistem pengolahan data jalur kereta api dengan menggunkan image processing bisa menampailkan respon visual hingga ukuran cm, respon tersebut muncul jika ada pergeseran jalur maka sistem langsung memberikan data berupa lokasi dan pergeseran jalur pada komputer utama, sistem ini lebih cepat dalam mengecek dan menganalisa data jalur kereta dengan akurasi dan presisi yang tinggi hingga 90%, selain citra dari satelit pengambilan gambar bisa menggunakan drone, drone sendiri sangat mudah dalam perawatan dan penggunaan serta mampu memangkas biaya produksi bahkan kecelakaan kerja petugas dilapangan sendiri bisa di hindari karena drone mampu menjangkau jalur dan rel kereta api yang sulit misalkan melewati terowongan atau derah rel dijalur perbukitan dan padat penduduk.
Metode Grayscale Co-occurrence Matrix (GLCM) Untuk Klasifikasi Jenis Daun Jambu Air Menggunakan Algoritma Neural Network Suhendri Suhendri; Putri Rahayu
Journal of Information Technology Vol 1 No 1 (2019): JoinT (Journal of Information Technology)
Publisher : LPPM STMIK AMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47292/joint.v1i1.4

Abstract

The color and shape of leaves each different plant water rose so that it can be found a certain texture to classify. This study uses an image texture recognition leaves to be classified. Leaves used are three types of guava leaves, Bunton 3 Green Guava, Guava and Guava image Bol. Feature extraction process used a method is Gray Level Co-Occurrence Matrix (GLCM) with Matlab tool. GLCM is used to retrieve the value of the image attribute or value matrix. This study uses a Neural Network algorithm with a tool RapidMiner. One alternative solution to the above problems is by way of classifying types of guava leaf water by looking at the characteristics of the water guava leaves. Leaf is one of the characteristics of the plant that is easily recognizable. The classification process is to produce a good accuracy value against bunton guava leaves 3 green, pink bol, and guava image. The results showed that the level of accuracy in the guava leaf bol is 81.25%, bunton leaves 3 Green 75%, and 80% leaf image and the total value of the overall accuracy of 78.89%. Thus the above results show that the value of the accuracy of the resulting research shows three types of guava leaf water has been classified and deserves to be investigated.
Peningkatan Sistem Keamanan Parkir dengan Teknologi Artificial Intelligence Imaging Ari Purno Wahyu; Suhendri Suhendri; Heri Heryono
Journal of Information Technology Vol 1 No 2 (2019): JoinT: Journal of Information Technology
Publisher : LPPM STMIK AMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47292/joint.v1i2.9

Abstract

Parking space is a public facility available at an agency or office that serves to temporally store vehicles, the vehicle that enters the parking area is become tens or even thousands, because tahat reason parking system and management area is needed. Such arrangements able parking procedures and even other support systems such as adequate parking facilities and infrastructure, other functions is making and developing parking systems in general for provide security and comfort, bacause the condition vehicle will be well organized in terms of vehicle placement and security and safety can be used for 24 hours. Constraints this time increasing number of vehicles requires a wider parking area or space, the slow pace of vehicle data collection because the technology used is still carried out vehicle license plate validation manually, another problem is the placement of large areas, this limitation is based on the number of parking attendants in the field is very limited, so extra time are needed to arrange and check the vehicles that have entered the parking area. This problem can be handle using image processing and OCR algorithm techniques, this technique has been implemented in several developed countries that are used to manage they parking system, image processing is used to record and monitor the number of vehicles in the area by reading the number plate, scanning techniques using OCR (Optical Character recognition techniques) , data from a vehicle plate image is converted into text or numbers and can be stored inside database, data from the vehicle plate that has been stored is then matched with a vehicle photo, with help the system can be integrated with the camera so that the supervision of the parking area can be carried out directly for a long time, the system is able to display data visually.
Sistem Informasi Pemeriksaan Jalur Kereta Api Menggunakan Drone dan Teknik Image Processing Siti Mardiana; Dani Hamdani; M Benny Chaniago; Ari Purno Wahyu; Heri Heryono; Suhendri Suhendri
Journal of Information Technology Vol 2 No 1 (2020): JOINT: Journal of Information Technology
Publisher : LPPM STMIK AMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47292/joint.v2i1.002

Abstract

Train is the main mode of transportation that we often use, the train itself can be used as a tool for shipping goods and mobilizing passengers, this transportation is very unique and has its own path in the form of steel strings across from hundreds of kilometers, railroad bearing structures currently exist which uses concrete and wood, the railroad is very vital and is an important supporting facility. The process of railroad monitoring is complex and complicated, takes a long time, the previous method is simple and conventional by tracing the railroad tracks manually or using a geometric gauge mounted direl or also known as railpod, railpod will follow the rails and will provide report if there is a train track that is damaged, broken or shifted, this research will create an image-based monitoring system using drones as a track monitor, another way is to take pictures using satellite data that will provide clear information about road conditions before being passed by the train, the railroad data processing system by using image processing can display visual responses up to cm in size, the response appears if there is a shift in the path then the system directly provides data in the form of location and shifting paths on the main computer, this system is more c eTat in checking and analyzing train track data with high accuracy and precision up to 90%, in addition to imagery from satellite images can use drones, drones themselves are very easy in maintenance and use and are able to cut production costs and even workplace accidents in the field workers themselves can be avoided because the drone is able to reach the track and railroad that is difficult for example through the tunnel or the railroad track along the hills and densely populated.
Implementasi Algoritma Haar Cascade Pada Aplikasi Pengenalan Wajah Personel Febiannisa Utami; Suhendri Suhendri; Muhammad Abdul Mujib
Journal of Information Technology Vol 3 No 1 (2021): JOINT (Journal of Information Technology)
Publisher : LPPM STMIK AMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47292/joint.v3i1.45

Abstract

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel.This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition.
Optimization Of Image Processing Techniques In Developing Of Smart Parking System Bagus Alit Prasetyo; ARI Purno Wahyu Wibowo; Suhendri Suhendri
Journal of Information Technology Vol 3 No 1 (2021): JOINT (Journal of Information Technology)
Publisher : LPPM STMIK AMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47292/joint.v3i1.47

Abstract

A parking system is currently a necessity and a common facility found in the campus area of ​​buildings and shops, the management and supervision of this parking lot generally uses third party assistance both in terms of technology and facilities used. At first the parking lot system was still conventional based by using guards in front of the gate and manually recording the entry and exit of the vehicle, a more sophisticated technology was using a camera that was combined with ticketing assistance, officers would check with photos when the car entered and left and strengthened with the use parking tickets, the two technologies have been implemented in many places, the weakness of the system is that there is a need for more than one operator to record and criminal acts will be difficult to detect if a type of vehicle of the same color is stolen because the data from the photos are indistinguishable, To solve this problem, a parking system was created with the help of image processing techniques by recording different types of vehicles even though they were of the same type and color, this system worked using computer vision algorithm rocks with a combination of OCR a Algorithm and surf algorithm, these two algorithms will help record vehicle number-plates while the surf algorithm will record the unique characteristics of the vehicle object in detail so that it will not be change.
Machine Learning Penyortiran Buah Naga Menggunakan Algoritma K-Means Berbasis Internet of Things Menggunakan Platform Blynks Ramadan, Wanda; Abidin, Aa Zezen Zenal; Suryadi, Usep Tatang; Murdianingsih, Yuli; Faizal, Muhammad; Suhendri, Suhendri; Carkiman, Carkiman
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 1 (2025): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i1.285

Abstract

Salah satu tahapan dalam proses pengelolaan hasil pertanian dan perkebunan ialah dengan melakukan pembagian terstruktur mengenai produk untuk menentukan kualitas hasil panen. Penyortiran dilakukan dengan melihat kualitas rona kulit, berat buah serta mengetahui jumlah satu kali panen. Kualitas buah naga ditentukan oleh berbagai parameter, antara lain umur dan kematangan (indeks warna), ukuran, dan berat buah. Sebagai salah satu komoditas yang disukai banyak orang, buah naga memerlukan proses sortasi (seleksi), karena pasar membutuhkan kondisi keseragaman buah naga. Seleksi biasanya dilakukan menurut prinsip pemisahan, seperti: bobot yang berbeda, bentuk yang berbeda, sifat permukaan yang berbeda, berat jenis yang berbeda, tekstur warna yang berbeda dan kematangan yang berbeda. Dalam proses penyortiran manual, manusia memiliki kelemahan dalam melakukan tugas sensorik dengan kapasitas besar dan jam kerja yang Panjang. Berangkat dari permasalahan tersebut penulis tertarik untuk membuat alat yaitu Machine Learning Penyortiran Buah Naga Berbasis Internet of Things Menggunakan Algoritma K- Means Pada Platform Blynk. Metodologi yang digunakan penulis diantaranya Studi pustaka, dokumentasi, data mining, analisa sistem, perancangan sistem, pembuatan sistem, pengujian sistem. Machine Learning Penyortiran Buah Naga Berbasis Internet of Things Menggunakan Algoritma K- Means Pada Platform Blynk yang penulis kerjakan dapat berhasil terealisasikan menggunakan sensor Load Cell untuk menghitung berat dan sensor TCS230 untuk menentukan warna. Serta sensor TCS3200 dapat mendeteksi warna dengan baik. Data yang didapat oleh alat dapat diklasterisasi menggunakan Algoritmaa K-Means dengan benar sebanyak 7 iterasi dengan nilai BCV=2096,84, WCV=442563,35, Rasio=211.
Implementation Of Finite State Automata In A Laundry Perfume Vending Machine For Clothes And Carpets Desvia, Yessica Fara; Pratama, Febryawan Yuda; Suhendri, Suhendri
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 2 (2025): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i2.296

Abstract

Perfume is popular among various groups of people, including laundry fragrances. Laundry perfumes come in a variety of scen$ts, such as fruity, floral, a combination of fruit and floral, and woody aromas. These fragrances are typically applied during the final stage of the laundry process. Currently, customers receive their laundry with a randomly selected scent based on the availability at the laundry service, which means they cannot choose the fragrance they prefer. Therefore, a Vending Machine (VM) design is needed to allow customers to select their desired laundry perfume. The VM is designed using the Finite State Automata (FSA) approach, specifically the Non-Deterministic Finite Automata (NFA) type, as it can accommodate multiple conditions for a single option. The development of the NFA method involves stages such as business process analysis, state diagram creation, VM design, and system testing. The results of this study indicate that the implementation of this VM simplifies the process for customers to choose their preferred laundry perfume, ensuring that their laundry has a scent that matches their personal preferences.
Co-Authors Abidin, Aa Zezen Zenal Ari Purno Wahyu Wibowo Arlen Nawang Rachelia Arlen Bagus Alit Prasetyo Carkiman, Carkiman Chaniago, M Benny Dani Hamdani Desvia, Yessica Fara Dina Hidayat Egi Sucianti Endang Ahmad Fanissa Narita Febiannisa Utami Febryawan Yuda Pratama Heri Heryono Heryono, Heri Indri Yani Lasmini Indri M Benny Chaniago Muhammad Abdul Mujib Muhammad Faizal Nico Irawan Novasa Adiyani Putri Rahayu Rahayu, Putri Ramadan, Wanda Siti Mardiana Siti Mardiana Usep Tatang Suryadi, Usep Tatang Warsono sudiro Yuli Murdianingsih, Yuli