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All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA Jurnal Ilmiah Teknologi dan Rekayasa TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) ComTech: Computer, Mathematics and Engineering Applications Jurnal Optimalisasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Ilmu Komputer dan Informatika
Suharjito Suharjito, Suharjito
Bina Nusantara University
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics

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Family Relationship Identification by Using Extract Feature of Gray Level Co-occurrence Matrix (GLCM) Based on Parents and Children Fingerprint Suharjito Suharjito; Bahtiar Imran; Abba Suganda Girsang
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 5: October 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (12.646 KB) | DOI: 10.11591/ijece.v7i5.pp2738-2745

Abstract

This study aims to find out the relations correspondence by using Gray Level Co-occurrence Matrix (GLCM) feature on parents and children finger print. The analysis is conducted by using the finger print of parents and family in one family There are 30 families used as sample with 3 finger print consists of mothers, fathers, and children finger print. Fingerprints data were taken by fingerprint digital persona u are u 4500 SDK. Data analysis is conducted by finding the correlation value between parents and children fingerprint by using correlation coefficient that gained from extract feature GLCM, both for similar family and different family. The study shows that the use of GLCM Extract Feature, normality data, and Correlation Coefficient could identify the correspondence relations between parents and children fingerprint on similar and different family. GLCM with four features (correlation, homogeneity, energy and contrast) are used to give good result. The four sides (0o, 45o, 90o and 135o) are used. It shows that side 0o gives the higher accurate identification compared to other sides.
Failure prediction of e-banking application system using adaptive neuro fuzzy inference system (ANFIS) Yuwono Abdillah; Suharjito Suharjito
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1199.981 KB) | DOI: 10.11591/ijece.v9i1.pp667-675

Abstract

Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and  increased accuracy by 1.27% compared to ANFIS without FCM optimization.
Effort Estimation Development Model for Web-Based Mobile Application Using Fuzzy Logic Stefani Agusta; Suharjito Suharjito; Abba Suganda Girsang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i5.6561

Abstract

Effort estimation becomes a crucial part in software development process because false effort estimation result can lead to delayed project and affect the successful of a project. This research proposes a model of effort estimation for web-based mobile application developed using object oriented approach. In the proposed model, functional size measurement of object oriented based web application named OOmFPWeb, web metric and mobile characteristic for web-based mobile application size measurement are combnined. The estimation process is done by using mamdani fuzzy logic method. To evaluate the proposed model, the comparison between OOmFPWeb as the variable that affect effort estimation for web-based mobile application and the proposed model are performed. The evaluation result shows that effort estimation for web-based mobile application with the proposed model is better than just using OOmFPWeb.
DETEKSI KEMATANGAN TANDAN BUAH SEGAR (TBS) KELAPA SAWIT BERDASARKAN KOMPOSISI WARNA MENGGUNAKAN DEEP LEARNING Muhammad Rifqi; Suharjito Suharjito
JURNAL TEKNIK INFORMATIKA Vol 14, No 2 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v14i2.23295

Abstract

Classification of oil palm fresh fruit bunch (FFB) based on maturity is very important for estimating oil content. Traditional methods using human vision to observe color changes during ripening and counting the number of fruits that fall from FFB are not effective. Research for neural architectures to design new network bases and improve them resulted in a set of models called EfficientNet. The most important function is the optimizer. This function repeatedly increases the parameters to reduce loss. In this study, the EfficientNetB0 and B1 models were developed to detect oil palm maturity into 6 classes, Raw, Ripe, Overripe, Underripe, abnormal, and empty bunch using optimizer RMSprop and SGD. From the research results, obtained the highest accuracy using the RMSprop optimizer of 0.9955 using the EfficientNetB0 model and 0.9949 using the EfficientNetB1 model. While using the SGD optimizer, the accuracy achieved is 0.918 using the EfficientNetB0 model and 0.9079 using the EfficientNetB1 model
Smart agriculture for optimizing photosynthesis using internet of things and fuzzy logic Abdul Latief Qohar; Suharjito Suharjito
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5467-5480

Abstract

Photosynthesis is a process that plants need. Plant growth requires sunlight to carry out photosynthesis. At night photosynthesis cannot be carried out by plants. This research proposes an internet of things (IoT) model that can work intelligently to maximize photosynthesis and plant growth using fuzzy logic. The plants used in this research are mustard plants because mustard plants are plants that have broad leaves and require more photosynthesis. The outputs of this proposed model are the activation of light emitting diodes (LED) lights and automatic watering based on input sensors such as soil moisture, temperature, and light intensity which are processed with fuzzy logic. The results show that the use of the IoT model that has been proposed can provide faster and better growth of mustard plants compared with mustard plants without an IoT system and fuzzy logic. This result is also strengthened by comparing the t-test between the two groups, with a significant 95% confidence level. The proposed model in this research is also compared with similar research models carried out previously. This research resulted in a plant height difference of 30.43% higher than the previous research. So, it can conclude that the proposed model can accelerate the growth of mustard plants.
Indonesian online travel agent sentiment analysis using machine learning methods Abimanyu Dharma Poernomo; Suharjito Suharjito
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp113-117

Abstract

Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment.  From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda
PERBAIKAN MODEL ALEXNET UNTUK MENDETEKSI KEMATANGAN TBS KELAPA SAWIT DENGAN MENGGUNAKAN IMAGE ENHANCEMENT DAN HYPERPARAMETER TUNING Indra Alfredo; Suharjito Suharjito
Jurnal Ilmiah Teknologi dan Rekayasa Vol 27, No 1 (2022)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2022.v27i1.5973

Abstract

Kualitas CPO yang baik adalah dihasilkan dari buah sawit yang mempunyai tingkat kematangan yang baik. Pada umumnya penentuan kematangan TBS kelapa sawit dilakukan melalui penilaian warna buah secara visual dan subjektif, sehingga perlu dikembangkan suatu model untuk mengidentifikasi tingkat kematangan berdasarkan karakteristik warna. Adapun tujuan dari penelitian ini adalah untuk mengembangkan sebuah model deep learning agar mendapatkan hyperparameter terbaik dari model yang diteliti yaitu AlexNet untuk mengklasifikasi tingkat kematangan. Jumlah dataset yang digunakan terdiri dari 6.000 buah gambar kelapa sawit dengan enam tingkat kematangan. Teknik augmentation akan digunakan untuk membantu memperbanyak jumlah dataset, selain itu menambahkan parameter image enhancement untuk mencerahkan gambar agar lebih nyata. Parameter lainnya menggunakan binary crossentropy untuk mengurangi loss dan optimizer menggunakan Stochastic Gradient Descent (SGD) untuk menemukan nilai optimal. Kemudian dari hasil evaluasi initial model dilakukan hyperparameter tuning untuk mendapatkan optimal parameter dari model AlexNet yang dibangun. Dari hasil penelitian ini menunjukkan bahwa model yang diajukan menggunakan metode Convolutional Neural Network (CNN) dengan menggunakan model AlexNet akurasi meningkat setelah menggunakan hyperparameter tuning dan image enhancement berhasil mencapai 0.9530.
Foreign exchange prediction based on indices and commodities price using convolutional neural network Rian Rassetiadi; Suharjito Suharjito
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp494-501

Abstract

The level of accuracy in predicting is the key in conducting forex trading activities in gaining profits. Some predictions are made only by using historical currency data to be predicted, this makes predictions less accurate because they do not consider external influences. This study examines external factors that can influence the results of predictions, by looking for the relationship between the value of indices such as NTFSE and S & P 500 and the value of commodities such as gold and silver to the prediction process of EUR / USD. Prediction carried out using a deep learning algorithm with the Convolutional Neural Network method uses 2 1-dimensional convolution layers with ReL activation. The data used is the value of Open, High, Low and Close prices on forex, indices and commodities which are combined into one with the close forex value target for the next 5 days. Testing of EUR / USD test data only gets MSE results of 0.00081894. While the results of testing of the combined test data between EUR / USD, indices and commodities producing MSE vary between 0.00068717 to 0.0109606 where the best combination is a combination of FTSE 100 and Natural Gas values. So it can be concluded that other factors included in predicting have an influence on the results obtained.
Analisis Prediksi Stroke dengan Membandingkan Tiga Metode Klasifikasi Decision Tree, Naïve Bayes, dan Random Forest Aulia, Yunita; Andriyansyah, Andriyansyah; Suharjito, Suharjito; Nensi, Sri Wahyu
Jurnal Ilmu Komputer dan Informatika Vol 3 No 2 (2023): JIKI - Desember 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.90

Abstract

Prediksi stroke telah muncul sebagai bidang penelitian dan intervensi kesehatan yang penting karena dampaknya yang signifikan terhadap kesehatan masyarakat dan kesejahteraan individu. Pemeriksaan rinci mengenai usia, hipertensi, penyakit jantung, status perkawinan, jenis pekerjaan, jenis tempat tinggal, rata-rata kadar glukosa, BMI, status merokok, dan jenis kelamin sebagai factor terjadinya stroke. Dengan melakukan sintesis penelitian dan menganalisis kumpulan data yang luas, penelitian ini bertujuan untuk menjelaskan hubungan rumit antara faktor-faktor tersebut dan dampak kumulatifnya terhadap risiko stroke. Metode penelitian ini diawali dengan perbandingan algoritma Decision Tree, Naïve Bayes dan Random Forest dengan menggunakan software RapidMiner. Dari dataset prediksi stroke yang diberikan, terdapat 5110 responden dengan kondisi beragam. Di antara 5110 responden tersebut terdapat 12 atribut. Berdasarkan uraian yang telah dibahas maka dapat diambil kesimpulan bahwa metode Decision Tree merupakan metode terbaik dengan nilai akurasi tertinggi sebesar 95,13% dibandingkan dengan metode Random Forest dan Naïve Bayes dan nilai TF (True False) yang dipilih adalah 4861, TT (True True) adalah 0, FF (False False) adalah 249, dan FT (False True) adalah 0.
Implementasi Prediksi Penyakit Jantung Menggunakan Data Mining Untuk Dunia Kesehatan Arta, Mikhael Chandra; Anwar, Nur; Putri, Yulia Aneke; Suharjito, Suharjito; Asroll, Muhammad
Jurnal Optimalisasi Vol 10, No 1 (2024): April
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jopt.v10i1.9075

Abstract

Jantung merupakan organ vital manusia yang sering menjadi penyebab kematian tertinggi. Penyakit jantung dapat diketahui dengan cara pemeriksaan dokter atau sejumlah tes kesehatan. Saat ini, perusahaan perlu memprediksi karyawannya yang kemungkinan memiliki riwayat atau calon pengidap penyakit jantung untuk mengurangi risiko kematian. Tindakan yang dilakukan dapat menggunakan pembelajaran mesin. Pembelajaran mesin memang dapat membantu dalam identifikasi awal penyakit dan meningkatkan hasil pengobatan. Sistem ini mampu memprediksi yang dapat membantu prediksi diagnosis penyakit jantung secara cepat dan akurat. Penelitian ini bertujuan untuk prediksi diagnosis penyakit jantung secara cepat dan akurat dengan menggunakan algoritma terbaik. Algoritma yang digunakan untuk melakukan prediksi yaitu Decision Tree, Gradient Boosted, dan Random Forest. Untuk prediksi, atribut yang digunakan adalah usia, jenis kelamin, tekanan darah, kolesterol, gula darah, detak jantung, jenis sakit dada dan tambahan adalah hasil pemeriksaan fisik lainnya. Dari hasil yang diperoleh, Gradient Boosted adalah algoritma yang memiliki AUC, presisi dan recall tertinggi dengan 86.6%.
Co-Authors Abba Suganda Girsang, Abba Suganda Abdul Latief Qohar Abimanyu Dharma Poernomo Andika, Rizky Andriyansyah, Andriyansyah Arta, Mikhael Chandra Asroll, Muhammad Astrid Lestari Tungadi Aulia, Yunita Demi Adidrana Dian Puspita Tedjosurya, Dian Puspita Halim, Marwa Ida Ratna, Ida Imran, Bahtiar Indra Alfredo Ishartati Jimmy Jimmy Josef Bernadi, Josef Leonardus Hardjo, Leonardus Maulivia, Vivia Surya Muhammad Rifqi Nensi, Sri Wahyu Nico Surantha, Nico Nur Anwar, Nur Perdana, Panji Arief Putri, Yulia Aneke Raymond Raymond Rian Rassetiadi Stefani Agusta Tinambunan, Gusmariani Widodo, Bambang Yundari, Yundari Yuwono Abdillah