Judul: Pengenalan Varietas Kunyit Berdasarkan Ciri Fisik Menggunakan Genetic Programming dengan Praproses Booleanizing
Abstrak: Turmeric is one of medicinal plant that can be used to treat swollen gums, sores, shortness of breath, abdominal pain, ulcers, sore spleen, appendix, gout, indigestion, flatulence, and lowers blood pressure. Turmeric have been processed through a selection in a breeding program and has acquired three varieties of turmeric with curcumin production potential and high quality, the variety of turmeric is Turina-1, Turina-2, and Turina-3. The pure seed requirement will increase and genetic diversity in turmeric growing community will be more narrow. This causes difficulties in distinguishing of high yielding varieties, as based on existing physical data Balittro almost identical, making it difficult to determine distinguishing characteristics. This research aims to develop an identification system to identify turmeric based on physical feature. This system identifies 3 variety of turmeric. The identification system being built uses Genetic Programming with Booleanizing preprocessing.The data are divided into three subset. The three subset are used as training data and test data. The Genetic Programming using 100 data to kinds of turmeric. The identification using Genetic Programing with Booleanizing preprocessing produced 95% accuracy.
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Judul: Identifikasi Varietas Kunyit Berdasarkan Ciri Fisik Menggunakan Algoritme C4.5
Abstrak: Turmeric is one type of medicinal plants which through a selection in the breeding program has acquired three varieties with high curcumin potential and high quality, the varieties are Turina-1, Turina-2, and Turina-3. Turmeric identification system can help farmers and traders in identifying varieties of turmeric quickly and accurately. Previous research implemented fuzzy algorithms to identify the varieties of turmeric, with accuracy reaching 85%. Another research used genetic programming with an accuracy of 95%. The present study use the C4.5 algorithm which is one of data mining classification methods which are used to identify varieties of turmeric plants by creating a decision tree by selecting the attributes that have the highest gain ratio as a node of a decision tree. The best accuracy obtained in this research is 95%. The advantages of using C4.5 algorithm is the decision tree produced is easy to understand and used manually
Keyword: varieties of turmeric plant, decision tree, C4.5 algorithm, data mining

Judul: The Effect of Ni Doped Level on MoS2 as Anode of Natrium Ion Batteries (NIB) on Adsorption and Diffusivity of Na Ions
Abstrak: Perkembangan teknologi elektronik dan kendaraan listrik menuntut ketersediaan baterai isi ulang dengan kapasitas dan ukuran yang besar. Hal ini menyebabkan semakin meningkatnya kebutuhan litium. Li ketersediaannya akan habis dan harganya akan melonjak tinggi. Natrium menjadi solusi karena karakteristiknya menyerupai Li, murah dan ketersediaannya melimpah. Namun, jari-jari kation Na lebih besar sehingga diperlukan modifikasi komponen baterai Na. Studi eksperimental sebelumnya menunjukkan bahwa monolayer MoS2 terdoping menghasilkan kinerja elektrokimia yang ditingkatkan dan kapasitas baterainya lebih tinggi dibandingkan dengan monolayer MoS2 murni. Nikel ditambahkan pada penelitian ini dengan cara doping karena ketersediaan Ni sangat melimpah. Melalui perhitungan DFT, penulis menyelidiki perilaku adsorpsi dan difusi ion Na pada MoS2 murni dan MoS2 terdoping Ni (dengan kadar 0%, 25%, 50% dan 100%). Pengaruh doping Ni pada MoS2 memiliki kelebihan dan kekurangan. Luas permukaan struktur sistem MoS2 terdoping Ni lebih kecil, sifat konduktivitas struktur sistem terdoping menjadi lebih baik, transfer muatan dari permukaan terhadap Na lebih besar, kapasitas penyimpanan baterai meningkat, ion Na teradsorpsi lebih baik karena energinya lebih negatif, namun, atom Na tidak terdifusi dengan baik karena energi aktivasi yang besar sehingga menyebabkan atom Na sulit untuk bergerak.
Keyword: adsorption, diffusion, DFT, Nickel, Sodium