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All Journal CESS (Journal of Computer Engineering, System and Science) INTEGER: Journal of Information Technology International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal INFOTEL
Sani, Dian Ahkam
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Sistem Pakar Untuk Mendiagnosa Penyakit Pada Perokok Aktif Dan Perokok Pasif Dengan Menggunakan Metode Anfis Safira, Laila; Misdram, Muhammad; Sani, Dian Ahkam
INTEGER: Journal of Information Technology Vol 6, No 1: Mei 2021
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2021.v6i1.1202

Abstract

Abstract. Cigarettes or cigars are cylinders of paper measuring between 70 and 120 mm long and about 10 mm in diameter containing chopped dried tobacco leaves. A person who inhales cigarette smoke is called a smoker. Smokers are divided into active smokers and passive smokers. An active smoker is someone who regularly consumes the smallest amount of cigarettes even though it's only 1 cigarette a day, and a passive smoker is someone who inhales cigarette smoke from an active smoker. Exposure to secondhand smoke can cause serious illness and death. The dangers of smoking on the health of the body have been researched and proven by many people. Lack of self-care, and lack of knowledge about the dangers of smoking make some people no longer think about their health in the future. Many rule out the bad effects caused by cigarette smoke. This is because these effects are not immediately visible when you first smoke. Many smokers are reluctant to get checked out for various reasons. Therefore, researchers made the implementation of an expert system to diagnose diseases in active smokers and passive smokers using the Anfis method. Anfis is an amalgamation of the system's fuzzy interface mechanism described in a neural network architecture. From the results of the implementation trial, the accuracy of the learning rate was 70% - 90% by including the same symptoms.Keywords: Expert System, Cigarettes, Anfis
Rancang Bangun Mobile Game Adventure Of Studies Sebagai Media Pembelajaran Ramadaniati, Setta; Sani, Dian Ahkam; Arif, Mochammad Firman
INTEGER: Journal of Information Technology Vol 6, No 1: Mei 2021
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2021.v6i1.1200

Abstract

Dalam   upaya   meningkatkan   efisiensi   penyediaan  aplikasi   yang   mengandung   unsur pendidikan  maka diperlukan  berbagai  alternatif  dan  inovasi  baru  dalam  hal  pemrograman untuk  bisa  diterapkan  sebagai  alat  untuk  mempermudah  proses  pembelajaran.  Dengan adanya  Rancang Bangun Mobile Game Adventure Of Studies sebagai Media Pembelajaran  ini  diharapkan  untuk  meningkatkan  kemampuan  berfikir  anak dalam  proses  belajar,  bahwa  game  ini  sangat  berguna  di  bidang  pendidikan. Penyusun    memilih    anak-anak   dan remaja sebagai    target    pemain, karena    kesulitan    proses pembelajaran secara teoritis yang di ajarkan pada anak-anak dan remaja, mengingat mereka lebih suka bermain. Metode penelitian yang digunakan adalah metode penelitian dan pengembangan (Research and Development) yang menggunakan tahapan penelitian Analisis Kebutuhan, Desain dan Perancangan Game, Pengembangan Game, Implementasi atau Uji Kelayakan Game, Evaluasi Hasil. Pembuatan game dilakukan dengan menggunakan game engine Construct 2. Penerapan  game media pembelajaran   ini   diharapkan   dapat   mengatasi   masalah   tersebut.   Sehingga   di   saat   anak memainkan game ini secara tidak langsung dapat belajar, dengan harapan semangat anak untuk belajar akan lebih terpacu dan meningkatkan kualitas belajar anak.
Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier Sarwani, Mohammad Zoqi; Salafudin, Muhammad Shubkhan; Sani, Dian Ahkam
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 1 (2020): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2169.831 KB) | DOI: 10.25139/ijair.v2i1.2636

Abstract

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.
An Implementation of MMS Steganography With The LSB Method Sani, Dian Ahkam; Sarwani, Mohammad Zoqi; Setiawan, Muhamad Agus
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 1 (2020): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1378.483 KB) | DOI: 10.25139/ijair.v2i1.2653

Abstract

Around the world, the internet (interconnection network) has developed into one of the most popular data communication media. With a variety of illegal information retrieval techniques that are developing, many people are trying to access information that is not their right. Various techniques to protect confidential information from unauthorized persons have been carried out to secure important data. Steganography is a science and art for writing hidden messages so that no other party knows the existence of the message. The three results of tests conducted by the LSB method can be used to hide messages into images. The first test was successful by writing a message that less than 31 characters stored in the picture, the second succeeded in writing a message equal to 31 characters stored in the picture, the third failed to write a message of more than 31 characters stored in the picture.
Switching Systems Designing Based on IoT Sani, Dian Ahkam; Fijriyah, Immilda Lailatul
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 2 (2020): IJAIR : November
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2039.464 KB) | DOI: 10.25139/ijair.v2i2.3138

Abstract

The development of technology has progressed very rapidly in a short period, as has technology that has recently been developed in various aspects of life, namely the Internet of Things. In the past, controlling household electrical appliances was usually done directly by pressing a button on the house's wall and was very ineffective when the house owner was out of town while the house was empty. With the Internet of Things technology, a system can be applied in everyday life, namely controlling household electrical appliances to turn off and remotely using internet communication via an android smartphone. In this system design, a control design using a series of microcontrollers and relays connected to a smartphone via the internet is used because the microcontroller already has a  Wireless Fidelity (WIFI) module. The results of controlled tests on household electrical appliances can run well. All components of the design of the device are well integrated with smartphones and the internet. Control can be done anywhere and anytime. System response during the day between 1-4 seconds and at night between 1-2 seconds.
Klarifikasi Status Penderita Gizi Sunting Pada Balita Menggunakan Metode Random Forest Aprilia, Yunita Nur; Sani, Dian Ahkam; Anggadimas, Nanda Martyan
INTEGER: Journal of Information Technology Vol 9, No 2: September 2024
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v9i2.6080

Abstract

Stunting in children, as in this case, is characterized by lower-than-average body growth. This is caused by a mismatch between long-term nutrient intake and the body's needs. Possible impacts include delayed cognitive development, impairments in learning ability, as well as an increased risk of metabolic syndrome. To overcome these problems, a structured and data-based system is needed with one of the agreements used, namely the Random Forest Method on the system using stunting nutrition data for toddlers as the basis for the classification process. In developing the system that was built to help track the health of young children, especially stunting by using several indicators to support innovation, provide a classification model for toddlers suffering from stunting nutrition, and measure and evaluate the performance results of the Random Forest Method against the data variables used. From this study, it can be shown that the results of this study are that this system has successfully made a classification model and is very effective in measuring and evaluating the performance results of the Random Forest Method in the Status Classification of Stunting Nutritional Patients in Toddlers by using a dataset of 300 data so that it produces an average accuracy of 81%, an average result of 76%, an average recall result of 69%, and the average F1 score result is 72%.
Klasifikasi Citra Penyakit Daun Padi Dengan Metode CNN Menggunakan Arsitektur ResNet50V2 Maulana, Muhamad Filla Akbar; Anggadimas, Nanda Martyan; Sani, Dian Ahkam
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 2 (2025): Juli 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i2.66960

Abstract

Tanaman padi sangat penting untuk menjaga ketersediaan pangan di Indonesia. Namun, produksinya sering menurun karena berbagai masalah, salah satunya yaitu penyakit daun yang sulit dikenali sejak awal. Jika penyakit tidak cepat dikenali dan ditangani, hal ini bisa menyebabkan hasil panen menurun drastis dan mengganggu pasokan beras di dalam negeri. Oleh karena itu, penelitian ini bertujuan untuk pengembangan sistem identifikasi penyakit daun padi otomatis. Sistem ini memanfaatkan citra digital dan jaringan saraf tiruan Convolutional Neural Network (CNN) dengan arsitektur ResNet50V2 untuk mengenali delapan jenis penyakit: Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Narrow Brown Spot, Rice Hispa, Sheath Blight, dan Tungro. Data citra diperoleh dari platform Kaggle, dengan total 15.241 gambar yang telah melalui tahapan preprocessing seperti normalisasi piksel, augmentasi, dan pengubahan ukuran menjadi 224x224 piksel. Model CNN dilatih menggunakan pendekatan transfer learning selama 50 epoch dengan bantuan dua fitur callback untuk menjaga kualitas pelatihan. Evaluasi performa dilakukan melalui confusion matrix dan classification report. Berdasarkan hasil pengujian, model menunjukkan akurasi tertinggi sebesar 94,14% pada data uji, serta nilai precision, recall, dan f1-score yang tinggi di hampir seluruh kelas. Dari hasil ini membuktikan bahwa CNN berbasis ResNet50V2 efektif digunakan untuk mendeteksi penyakit daun padi secara otomatis, dan berpotensi diterapkan sebagai alat bantu bagi petani dalam mempercepat proses identifikasi dan pengambilan keputusan di bidang pertanian.
A Random Oversampling and BERT-based Model Approach for Handling Imbalanced Data in Essay Answer Correction Sani, Dian Ahkam
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1224

Abstract

The task of automated essay scoring has long been plagued by the challenge of imbalanced datasets, where the distribution of scores or labels is skewed towards certain categories. This imbalance can lead to poor performance of machine learning models, as they tend to be biased towards the majority class. One potential solution to this problem is the use of oversampling techniques, which aim to balance the dataset by increasing the representation of the minority class. In this paper, we propose a novel approach that combines random oversampling with a BERT-base uncased model for essay answer correction. This research explores various scenario of text pre-processing techniques to optimize model accuracy. Using a dataset of essay answers obtained from eighth-grade middle school students in Indonesian language, our approach demonstrates good performance in terms of precision, recall, F1-score and accuracy compared to traditional methods such as Backpropagation Neural Network, Naïve Bayes and Random Forest Classifier using FastText word embedding with Wikipedia 300 vector size pretrained model. The best performance was obtained using the BERT-base uncased model with 2e-5 learning rate and a simplified pre-processing approach. By retaining punctuation, numbers, and stop words, the model achieved a precision of 0.9463, recall of 0.9377, F1-score of 0.9346, and an accuracy of 94%.
Co-Authors Anggadimas, Nanda Martyan Aprilia, Yunita Nur Fijriyah, Immilda Lailatul Maulana, Muhamad Filla Akbar Misdram, Muhammad Mochammad Firman Arif Mohammad Zoqi Sarwani, Mohammad Zoqi Ramadaniati, Setta Safira, Laila Salafudin, Muhammad Shubkhan Setiawan, Muhamad Agus Suharyadi, Heri